Master's thesis of Science: User expectations of a national, high-accuracy GNSS positioning service for professional applications: an Australian context

User expectations of a national, high-accuracy GNSS positioning service for

professional applications: an Australian context

A thesis submitted in fulfilment of the requirements for the degree of Master of Science

Luis Elneser Gonzalez

B.Eng. Geodetic Engineering. (La Universidad del Zulia)

School of Science

College of Science, Engineering and Health

RMIT University

March 2020

DECLARATION

I certify that except where due acknowledgement has been made, the work is that of the author

alone; the work has not been submitted previously, in whole or in part, to qualify for any other

academic award; the content of the thesis is the result of work which has been carried out since

the official commencement date of the approved research program; any editorial work, paid or

unpaid, carried out by a third party is acknowledged; and, ethics procedures and guidelines

have been followed.

I acknowledge the support I have received for my research through the provision of an

Australian Government Research Training Program Scholarship.

Luis Elneser Gonzalez

08 March 2020

ACKNOWLEDGEMENTS

It has been an honour to work with my supervisors, Dr Suelynn Choy and James Millner. I am

profoundly grateful to them for lending their guidance, support, and vision in this research. I

am also grateful to Dr Ken Harima for sharing his expertise throughout this project. Likewise,

I give thanks to the rest of the geospatial research team and staff in RMIT for their help.

I would like to acknowledge the support from the Commonwealth Government Research

Training Scheme and the FrontierSI top-up scholarship. I am thankful to Phil Collier, Nathan

Quadros, Eldar Rubinov and all the team in FrontierSI for their assistance and inspiration.

I had the chance to work with great people and companies during this project. I would like to

thank Lance Wallace, Brent Speed and the team at Energy Australia who helped carry out the

Yallourn Mine trials. Thanks to Tom Wyatt, William McCarthy, Andrew Bate and the people

in SwarmFarm Robotics for assisting with the robotic tractor trials. Many thanks to Shannon

Cox and Andrew Quill from Tas Rail, Paul Reichl from Monash University and the FrontierSI

SBAS testbed project for enabling the rail test.

I was fortunate to receive insights and assistance from many other industry leaders. Special

thanks to Chris Lehnert, Bilal Arain, Nigel Zweck, Josh Allan, Herm Chwa, Chris Harris,

Matthew Nicholas, Christian Wullems, Shien-Kwun Leong, Jose-Angel Avila-Rodriguez and

Grant Hausler.

While doing this Masters, I counted on the patience and support from Position Partners. I thank

them for helping me juggle full-time work and explore this research.

I could not be typing these words without the love and support of my family. Luz, whose

wisdom and encouragement motivates all around her to be the best humans we can be. Amaya

and Noemi, who have the power to make the world simple and fun. Efrahin and Milagro, who

are models of strength and perseverance. Thank you all.

Declaration.............................................................................................................................................. i

Acknowledgements ............................................................................................................................... ii

Table of Contents ................................................................................................................................. iii

List of Figures ...................................................................................................................................... vii

List of Tables ........................................................................................................................................ ix

List of Abbreviations ........................................................................................................................... xi

Abstract .................................................................................................................................................. 1

Chapter 1: Introduction ....................................................................................................................... 2

1 Introduction ................................................................................................................................... 3

1.1 Background ................................................................................................................. 4

1.2 Justification ................................................................................................................. 5

1.3 Research Questions ..................................................................................................... 6

1.4 Project Outcomes ........................................................................................................ 7

1.5 Thesis Outline ............................................................................................................. 7

Chapter 2: Positioning and Navigation Methods ............................................................................. 10

2 Positioning and Navigation Methods ......................................................................................... 11

2.1 Overview of Positioning and Navigation .................................................................. 11

2.2 Satellite-Based Positioning and Navigation .............................................................. 12

2.2.1 GNSS ................................................................................................................. 12

2.2.2 RNSS.................................................................................................................. 18

2.3 GNSS Augmentation Methods .................................................................................. 22

2.3.1 GNSS Observables............................................................................................. 23

2.3.2 GNSS Representation Methods ......................................................................... 25

2.3.3 GNSS Augmentation Delivery Channels........................................................... 28

2.4 GNSS trends and Complementary Positioning Technologies................................... 32

2.4.1 GNSS Trends ..................................................................................................... 32

iii

2.4.2 Complementary Positioning Technologies ........................................................ 33

2.5 Summary ................................................................................................................... 34

Chapter 3: PNT Infrastructure ......................................................................................................... 35

3 PNT Infrastructure ..................................................................................................................... 36

3.1 Reference Frames and Coordinate Systems: From Global to Local ......................... 36

3.2 Geodetic Infrastructure: From Global to Local ......................................................... 38

3.2.1 Improving Density with User-owned GNSS Infrastructure............................... 42

3.2.2 The Importance of GNSS Infrastructure ............................................................ 44

3.3 PNT Infrastructure in Civil construction ................................................................... 46

3.4 PNT Infrastructure in Agriculture ............................................................................. 48

3.5 PNT Infrastructure in Rail Transport ........................................................................ 49

3.6 Summary ................................................................................................................... 49

Chapter 4: GNSS Augmentation Services for Professional High-Accuracy Applications .... 51

4 GNSS Augmentation Services for Professional High-Accuracy Applications ................ 52

4.1 GNSS Augmentation Services Marketplace ............................................................. 52

4.2 PPP Augmentation Services provided by GNSS and RNSS operators ..................... 54

4.3 Research and Government PPP Augmentation Services in Australia....................... 55

4.4 Commercial PPP Augmentation Services in Australia ............................................. 57

4.5 RTK and DGNSS Services in Australia .................................................................... 59

4.6 Summary ................................................................................................................... 60

Chapter 5: PNT Requirements for Professional High-Accuracy Applications ............................. 66

5 PNT Requirements for Professional High-Accuracy Applications ........................................ 67

5.1 PNT user segments .................................................................................................... 68

5.2 Review of PNT Requirements................................................................................... 70

5.2.1 Defining Performance Parameters for High-Accuracy Applications ................ 71

5.2.2 Radionavigation Plans ....................................................................................... 73

5.2.3 European GNSS Agency User Consultation Platform....................................... 74

5.2.4 Requirements Framework in Related Industries ................................................ 74

5.3 PNT Requirements in Civil construction .................................................................. 79

5.3.1 Surveying ........................................................................................................... 79

5.3.2 Automated Machine Control.............................................................................. 80

5.3.3 UAVs ................................................................................................................. 82

5.3.4 Asset Tracking and Process Automation ........................................................... 83

5.3.5 Gathering PNT User Requirements for Civil Construction ............................... 84

5.4 PNT Requirements in Agriculture............................................................................. 87

5.4.1 AMC for Precision Agriculture ......................................................................... 87

5.4.2 UAVs ................................................................................................................. 89

5.4.3 Livestock and Asset Tracking ............................................................................ 90

5.4.4 Robotic Applications in Agriculture .................................................................. 90

5.4.5 Gathering PNT User Requirements for Agriculture .......................................... 91

5.5 PNT Requirements in Rail Transport ........................................................................ 93

5.5.1 Safety Applications in Rail Transport................................................................ 93

5.5.2 Operational Applications in Rail Transport ....................................................... 96

5.5.3 Professional Applications in Rail Transport ...................................................... 96

5.5.4 Gathering PNT User Requirements for Rail Transport ..................................... 97

5.6 Summary ................................................................................................................. 101

Chapter 6: Evaluation of PNT Services for Professional High-Accuracy Applications ............. 102

6 Evaluation of PNT Services for Professional High-Accuracy Applications ........................ 103

6.1 Civil construction and Mining Case Study ............................................................. 103

6.1.1 Experiment Design........................................................................................... 104

6.1.2 Performance Evaluation ................................................................................... 106

6.2 Agriculture Case Study ........................................................................................... 110

6.2.1 Experiment Design........................................................................................... 110

6.2.2 Performance Evaluation ................................................................................... 112

6.3 Rail Transport Case Study....................................................................................... 113

6.3.1 Experiment Design........................................................................................... 114

6.3.2 Performance Evaluation ................................................................................... 116

6.4 Summary ................................................................................................................. 118

Chapter 7: Conclusions and Recommendations ............................................................................ 119

7 Conclusions and Recommendations ........................................................................................ 120

7.1 Conclusions ............................................................................................................. 120

7.1.1 Positioning Methods, Infrastructure and Products ........................................... 120

7.1.2 User Requirements of Professional Industries ................................................. 122

7.2 Recommendations ................................................................................................... 123

References .......................................................................................................................................... 125

Appendix A: Accuracy Measures .................................................................................................... 145

Appendix B: Performance Parameters ........................................................................................... 149

LIST OF FIGURES

Figure 1-1 Research Project Outline ......................................................................................... 7

Figure 2-1 Orbital planes of GNSS. Adapted from Reid et al. (2013). .................................. 13

Figure 2-2 Frequency bands for GNSS and RNSS (Navipedia, 2014). .................................. 14

Figure 2-3 Current SBAS service areas. *ICAO certification in-progress. ............................ 22

Figure 2-4 Spatial representation of corrections. Adapted from Lachapelle et al. (2010). ..... 26

Figure 2-5 GNSS augmentation methods organised by accuracy and range. Adapted from

NovAtel Inc. (2017). ................................................................................................................ 27

Figure 3-1 ITRF four-technique colocation site in Yarragadee, Western Australia (Hu, 2014)

.................................................................................................................................................. 40

Figure 3-2 APREF CORS Yulara, Northern Territory (YULA) (Hu, 2014) .......................... 41

Figure 3-3 GPSNET CORS Lalber, Victoria (Hu, 2014) ....................................................... 41

Figure 3-4 Classification of GNSS monuments (UNAVCO, 2018) ....................................... 42

Figure 3-5 Density of RTK reference stations (GSA, 2017). ................................................. 43

Figure 3-6 Relationship between network density and RTK accuracy (Tsuji et al., 2018) .... 44

Figure 3-7 GNSS segments (Sabatini et al., 2017) ................................................................. 45

Figure 3-8 Construction site GNSS base station. .................................................................... 47

Figure 5-1 PNT User’s Perspectives 2025 (National Security Space Office, 2008) .............. 69

Figure 5-2 Cumulative revenue 2019–2029 of ‘professional’ segments (GSA, 2019a) ......... 70

Figure 5-3 Installed base of ‘professional’ segments 2019–2029 (GSA, 2019a) ................... 70

Figure 5-4 Positioning performance for 5G technologies in different environments

(Fraunhofer IIS, 2018) ............................................................................................................. 77

Figure 5-5 (a) Control screen of an excavator machine control system. (b) Excavator with

3D-GPS system. (c) Dozer with 3D-GPS system (Position Partners, 2020) ........................... 81

Figure 5-6 (a) Motor-grader using automatic blade control with robotic total station for fine

grading. (b) Tracked paver controlled by GNSS and Millimetre Laser system (Elneser

Gonzalez, 2016) ....................................................................................................................... 82

Figure 5-7 Active GNSS guidance of a seeding machine implement (Heege, 2013) ............. 88

Figure 5-8 Cotton yield monitor with optical flow sensors and DGNSS (Perez-Ruiz &

Upadhyaya, 2012) .................................................................................................................... 89

Figure 5-9 RIPPA™, the Robot for Intelligent Perception and Precision Application

(Australian Centre for Field Robotics, 2018) .......................................................................... 91

vii

Figure 5-10 Key requirements in agriculture. Adapted from GSA (2019a) ........................... 93

Figure 5-11 Examples of challenging operating environments: (a) Tracks with 3-m

separation. (b) Tunnels and obstructions. (c) High multipath and interference areas (Sabina et

al., 2018) .................................................................................................................................. 95

Figure 5-12 GNSS and railway performance requirement comparison (Lu & Schnieder,

2014) ........................................................................................................................................ 97

Figure 6-1 Stations used to calculate ionospheric delays. Location of field trials, Yallourn

Mine, Victoria. ....................................................................................................................... 104

Figure 6-2 a) CAT D11R dozer. b) Dozer GPS antenna. c) Existing and test GNSS receivers.

d) Dozer guidance system. e) Test receivers and signal splitter. f) Tablet with RTKLIB..... 105

Figure 6-3 Dozer ground track and East-West, North-South, Up-Down position plot for

testing ..................................................................................................................................... 106

Figure 6-4 Performance of Internet-delivered solutions for Test 1 ...................................... 107

Figure 6-5 Performance of Internet-delivered solutions for Test 2. ..................................... 108

Figure 6-6 Performance of Internet-delivered solutions for Test 3. ..................................... 108

Figure 6-7 Location of field trials, SwarmFarm Robotics, Gindie, Queensland. ................. 111

Figure 6-8 Robotic tractor with GNSS test equipment ......................................................... 112

Figure 6-9 Atlas H10 horizontal error for robotic tractor test; 10-cm error threshold in red 113

Figure 6-10 Location of field trials, TasRail’s Derwent Valley line, Tasmania ................... 114

Figure 6-11 TR11 locomotive and GNSS equipment installed for testing ........................... 115

Figure 6-12 Horizontal error of the Australian SBAS signal for the locomotive tests ......... 116

Figure 6-13 Track of TR11 run with obstructions and vegetation canopy ........................... 117

Figure A-1 Relationship between accuracy and precision. Adapted from van Diggelen,

2007........................................................................................................................................ 145

Figure A-2 Accuracy measures in a one-dimensional Gaussian probability distribution.

Adapted from van Diggelen, 2007.. ....................................................................................... 146

Figure A-3 Accuracy measures in a two-dimensional Gaussian probability distribution.

Adapted from van Diggelen, 2007.. ....................................................................................... 147

viii

LIST OF TABLES

Table 2-1 GPS signals and services overview ........................................................................ 15

Table 2-2 GLONASS signals and services overview ............................................................. 16

Table 2-3 BeiDou-3 signals and services overview ................................................................ 17

Table 2-4 Galileo signals and services overview .................................................................... 18

Table 2-5 QZSS signals and services overview ...................................................................... 20

Table 2-6 IRNSS signals and services overview .................................................................... 20

Table 2-7 Corrections used for different SSR solutions. Adapted from Choy & Harima

(2019) ....................................................................................................................................... 26

Table 4-1 Summary of PPP augmentation services provided by GNSS/RNSS operators ...... 62

Table 4-2 Summary of research and government PPP augmentation services delivered over

Internet ..................................................................................................................................... 62

Table 4-3 Summary of existing commercial PPP augmentation services available in Australia

.................................................................................................................................................. 63

Table 4-4 Summary of emerging commercial PPP augmentation services with potential

coverage in Australia ............................................................................................................... 64

Table 4-5 Summary of RTK and DGNSS positioning services with Internet delivery and

regional coverage in Australia ................................................................................................. 64

Table 4-6 Summary of user-owned RTK positioning services with local coverage in

Australia ................................................................................................................................... 65

Table 5-1 ICAO GNSS SIS performance requirements (ICAO, 2006) .................................. 75

Table 5-2 Positioning requirements for C-ITS (Austroads, 2013) .......................................... 76

Table 5-3 Sample of use case requirements for 5G positioning services (3GPP, 2018)......... 78

Table 5-4 Summary of construction tolerances from MRTS manuals .................................... 85

Table 5-5 GNSS user requirements for civil construction ...................................................... 86

Table 5-6 GNSS user requirements for agriculture ................................................................. 92

Table 5-7 Rail GNSS User Requirements. Adapted from GSA (2018b) ................................ 99

Table 5-9 GNSS user requirements for rail transport ........................................................... 100

Table 6-1 Test sessions for real-time dozer positioning ....................................................... 105

Table 6-2 Convergence time and performance of test sessions ............................................ 109

Table 6-3 Horizontal performance of Atlas H10 solution for kinematic run on board a tractor

................................................................................................................................................ 113

Table 6-4 Test sessions for the locomotive test .................................................................... 116

Table 6-5 Performance of Australian SBAS signal during locomotive tests ........................ 118

Table A-1 One-dimensional accuracy measures for Gaussian distribution .......................... 146

Table A-2 Two-dimensional accuracy measures for Gaussian distribution ......................... 147

Table A-3 Three-dimensional accuracy measures for Gaussian distribution ....................... 147

Table A-4 Interrelation of accuracy measures for Gaussian distribution .............................. 148

LIST OF ABBREVIATIONS

3GPP 3rd Generation Partnership Project

ACMA Australian Communications and Media Authority

ACS Analysis Centre Software

AES Advanced Encryption Standard

AMC Automated machine control

AMG Automated machine guidance

APAC Asia-Pacific Region

APREF Asia–Pacific Reference Frame

AR Ambiguity resolution

ARGN Australian Regional GNSS Network

ARTC Australian Rail Track Corporation

ASAS African Satellite Augmentation System

ATC Automatic train control

ATCS Automatic train control system

ATMS Advanced Train Management System

ATP Automatic train protection

ATRF Australian Terrestrial Reference Frame

Business-to-business B2B

BeiDou Navigation Satellite System BDS

BDSBAS BeiDou Satellite-based Augmentation System

Bundesamt für Kartographie und Geodäsie BKG

Chinese Academy of Sciences CAS

CBTC Communications-based train control

CDMA Code-division multiple access

C-ITS Cooperative-intelligent transport systems

CL Civil Long

CLAS Centimetre-level Augmentation Service

CM Civil Moderate

CNES Centre National d’Études Spatiales

CORS Continuously Operated Reference Stations

CTF Controlled traffic farming

DFMC Dual frequencies and multiple constellations

DGNSS Differential global navigation satellite systems

DHS Department of Homeland Security

DLR Deutsches Zentrum für Luft- und Raumfahrt

DoD Department of Defense

DORIS Doppler Orbitography and Radiopositioning Integrated by Satellite

DoT Department of Transport

DTM Digital terrain model

EC European Commission

EGNOS European Geostationary Navigation Overlay Service

ERTMS European Railway Traffic Management System

ESA European Space Agency

FDMA Frequency division multiple access

GAGAN GPS-aided GEO Augmented Navigation

GBAS Ground-based augmentation system

GDA Geocentric Datum of Australia

GEO Geostationary Earth orbit

GFZ GeoForschungsZentrum

GLONASS Globalnaya Navigatsionnaya Sputnikovaya Sistema

GNSS Global navigation satellite system

GPS Global Positioning System

GSA European Global Navigation Satellite Systems Agency

GTRF Galileo Terrestrial Reference Frame

HAL Horizontal alert limit

HAS High-accuracy Service

I In-phase

IBSS Internet Base Station Service

xii

ICAO International Civil Aviation Organization

Institute of Electrical and Electronics Engineers IEEE

International GNSS Service IGS

IGSO Inclined geosynchronous orbit

Inertial measurement unit IMU

Inertial navigation system INS

Internet of Things IoT

IRNSS Indian Regional Navigation Satellite System

International Terrestrial Reference Frame ITRF

International Telecommunications Union ITU

JAXA Japan Aerospace Exploration Agency

JPL Jet Propulsion Laboratory

KASS Korean Augmentation Satellite System

Key performance indicator KPI

Location-based services LBS

Low Earth orbit LEO

L-band Experimental LEX

Low frequency LF

LiDAR Light detection and ranging

Line-of-sight LOS

Long-term evolution LTE

MADOCA Multi-GNSS Advanced Demonstration Tool for Orbit and Clock Analysis

MEO Medium Earth orbit

MF Medium frequency

MOPS Minimum Operational Performance Specification

MRTS Main Roads Technical Specification

MSAS Multi-functional Satellite Augmentation System

NavIC Navigation with Indian Constellation

NFC Near-filed communication

NMA Navigation message authentication

xiii

NPIC National Positioning Infrastructure Capability

NRTK Network real-time kinematic

NTRIP Networked Transport of RTCM via Internet Protocol

OCDS Orbit and Clock Determination System

OS Open Service

OSR Observation-space representation

PNT Positioning, navigation and timing

PPP Precise point positioning

PPP+Ion Ionosphere-assisted PPP

PPP-AR Precise point positioning with ambiguity resolution

PPP-RTK Precise point positioning based on real-time kinematic networks

PPS Precise Positioning Service

PRS Public Regulated Service

Q Quadrature-phase

QZSS Quasi-Zenith Satellite System

RAMS Reliability, availability, maintainability and safety

RF Radio frequency

RMS Root mean square

RNP Required navigation performance

RNSS Regional navigation satellite systems

RS Restricted Service

RTCM Radio Technical Commission for Maritime Services

RTK Real-time kinematic

SACCSA Sistema de Aumentación para el Caribe, Centro y Sudamérica

SAE Society of Automotive Engineers

SAR Search and rescue

SBAS Satellite-based augmentation systems

SDCM System for Differential Corrections and Monitoring

SIL Safety integrity level

SIS Signal-in-space

xiv

SISRE Signal-in-space range error

SLAS Sub-metre-level Augmentation Service

SLR Satellite laser ranging

SMCS Short message communication service

SoL Safety-of-Life

SPS Standard Positioning Service

SSR State-space representation

SSR-RTK RTK based on state-space representation

STL Satellite Time and Location

TTA Time-to-alert

TTFF Time-to-first-fix

UAV Unmanned aerial vehicle

UE User equipment

UHF Ultra-high frequency

US United States

VAL Vertical alert limit

VAR Value-added reseller

VLBI Very-long-baseline interferometry

VRA Variable-rate application

WAAS Wide-Area Augmentation System

WADGNSS Wide area differential global navigation satellite systems

WGS World Geodetic System

ABSTRACT

Global navigation satellite systems (GNSS) are hidden utilities that are critical to the

functioning of modern society. GNSS delivers positioning, navigation and timing (PNT)

services that benefit a wide range of users in the scientific, public and industrial sectors. The

Positioning Australia programme aims to provide the infrastructure and capability required to

deliver open, high-accuracy positioning services across Australia. Therefore, it is essential to

study the requirements of users who will benefit from these services. In this context, this

research aims to review the positioning needs of users in professional applications within

Australia, in particular, civil construction, precision agriculture and rail transport.

The upcoming modernisation of GNSS and increasing demands of mass-market users will blur

the lines between standalone, augmented, open and commercial PNT services. This research

examines the current and future state of GNSS positioning techniques, infrastructure, and

services. Government initiatives, such as the Australian Satellite-Based Augmentation System

(SBAS), will deliver open-access precise point positioning (PPP) and will coexist with a

growing market of commercial service providers. The marketplace of GNSS services will need

to adapt to this modernisation while still complying with the demanding PNT requirements of

professional users.

This study presents a literature review of PNT requirements across industries. It proposes a

requirements framework for three professional sectors that are being transformed by

automation, connectivity and high-accuracy positioning. The PNT requirements of

construction, agriculture and rail applications are reviewed and further validated by field trials.

Some of the most demanding applications in construction and agriculture require positioning

accuracies that can only be achieved by real-time kinematic (RTK) and complementary

positioning technologies, such as inertial navigation system (INS) or optical tracking.

The research findings highlight that integrity, connectivity and latency are essential

requirements for applications such as real-time tracking, robotic and safety-related

applications. The rail industry, in particular, has demonstrated that GNSS can be applied to

safety-related applications. However, further policy and regulatory work is required to

implement its use across the sector. This research presents its findings and recommendations

with the aim to inform the Positioning Australia programme.

CHAPTER 1: INTRODUCTION

1 INTRODUCTION

As at March 2020, the global navigation satellite systems (GNSS) space had over 150

operational satellites in orbit. Constellations from the United States (US) Global Positioning

System (GPS), Russia’s Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS),

Europe’s Galileo, China’s BeiDou Navigation Satellite System (BDS), Japan’s Quasi-Zenith

Satellite System (QZSS), the Indian Regional Navigation Satellite System (IRNSS) and other

satellite-based augmentation systems (SBAS) will provide new signal combinations and

improved geometries referred to as ‘multi-GNSS’ (Montenbruck et al., 2014; Rizos, 2008).

The combination of these systems promises to deliver improved robustness, continuity,

accuracy, reliability and availability to the growing positioning, navigation and timing (PNT)

services industry worldwide.

Australia is situated in a key geographic location—a so-called ‘hotspot’ of GNSS visibility—

to take advantage of these emerging satellite systems and related augmentation services. To

address research challenges and realise the potential benefits of these future positioning

services, the Australian Government developed its Positioning Australia programme

(Geoscience Australia, 2019a). This programme aims to deliver instantaneous, reliable and fit-

for-purpose PNT anywhere and anytime across the Australian landscape through the National

Positioning Infrastructure Capability (NPIC) and Australian SBAS projects (Johnston &

Hausler, 2015; Geoscience Australia, 2019b).

Research and policy efforts are coming together to define a national high-accuracy GNSS

positioning service in Australia (Choy & Harima, 2019; Ernst & Young, 2019). Likewise,

GNSS operators in Europe, Japan and China are developing high-accuracy services for their

respective regions (Fernández-Hernández et al., 2018; Kogure et al., 2017; China Satellite

Navigation Office, 2016). However, the link between service performance and end-user

requirements has not yet been clearly defined.

The current study focuses on the user end of the Positioning Australia concept. It reviews the

various GNSS and non-GNSS positioning methods available for users. It also studies the

importance of communication and GNSS infrastructure. Further, it analyses the gaps and

opportunities for new business models in the marketplace of service providers. More

importantly, it presents a review of positioning requirements for three user segments: civil

construction, precision agriculture and rail transport. These industries represent a subset of the

professional high-accuracy market that is being transformed by positioning, connectivity and

automation, three broader trends that provide context to this study.

1.1 BACKGROUND

GNSS augmentation techniques are used by downstream markets of commercial and public

GNSS services to provide solutions for a wide range of PNT users. On its own, standalone

GNSS is a standard PNT method that delivers reliable, worldwide positioning, accurate to

within several metres. In addition, the aviation industry has promoted complementary systems

such as SBAS, which augment the integrity, reliability and accuracy of GNSS down to metre

level. For more precise applications, typically in the scientific and professional sectors, users

rely on additional techniques like differential GNSS (DGNSS), real-time kinematic (RTK),

network RTK (NRTK) and precise point positioning (PPP) to achieve decimetre- to centimetre-

level accuracies. These augmentation methods are described in Section 2.3.

GNSS users are divided into mass-market and professional market users. These markets are

further divided into individual industry sectors, as in the 2019 European GNSS Agency (GSA)

GNSS Market Report (GSA, 2019a). One of the main differences between segments is market

size: the mass-market contributes to 93.3% of PNT predicted cumulative revenue (2019–29)

and the professional market accounts for the remaining 6.7%. Nonetheless, the importance of

the professional market cannot be understated. Professional sectors have been the enabler and

driver of high-accuracy PNT, and several industries are dependent on it. In Australia,

augmented GNSS has delivered significant economic and social benefits to key industry sectors

and contributed to emerging applications, specifically in the automation of cooperative

intelligent transport systems (C-ITS), mining, machine guidance, integrated guidance systems

or robotics and precision farming (ACIL Allen Consulting, 2013a).

Professional end-users have defined the market for commercial GNSS augmentation services.

For example, offshore positioning is one application that requires improved performance

relative to that achievable by standalone GNSS. Commercial service providers have filled this

gap by offering augmented GNSS products that meet the requirements of this and other niche

markets. Services for high-accuracy applications, such as guidance and automatic control of

heavy machinery in the civil construction and agriculture sectors, also exist in a well-

established and competitive marketplace. Both the markets and end-user segments are set to

benefit from the upcoming public access augmentation services from GNSS system providers

and the Positioning Australia programme.

Alongside GNSS, there are steady improvements in complementary positioning technologies

such as inertial measurement units (IMUs), light detection and ranging (LiDAR), radar, sonar

and 3D vision. These have led to an increase in integrated, multi-sensor hardware that is now

commonplace in applications like robotics, unmanned aerial vehicles (UAVs) and autonomous

vehicles. These applications share the characteristic that they are being accelerated by

technological advances in automation, connectivity and positioning. The popularity of these

applications is growing among professional users and can potentially define the future GNSS

services market. It is necessary to place GNSS within the broader context of automation and

connectivity for end-user applications to study the end-user requirements for a modern PNT

solution.

Automation, connectivity and positioning in professional sectors deliver increased benefits in

terms of productivity and safety. Several industries are growing dependent on these

technologies as aging processes adapt to more efficient and safer work methods. This growth

is evident in the construction sector in particular, where the adoption rate of GNSS-guided

machines is projected to reach 40–60% by 2030 (ACIL Allen Consulting, 2013b). The

automation of machines has also been applied to mining and agriculture operations where the

trends are moving towards fully automated machine tasks and processes (Billingsley et al.,

2008; Corke et al., 2008; Australian Mining, 2015). Fully automated machines in isolated

environments such as farms, mines, construction sites and rail corridors are relatively easy to

implement compared with autonomous vehicles that operate in public spaces and interact with

a wide range of agents. However, they have not been considered explicitly in a PNT user

requirements framework.

1.2 JUSTIFICATION

The Australian Government, in its Satellite Utilisation Policy, has identified the NPIC as

critical infrastructure to develop space applications and deliver PNT products to Australian

industries (Australian Government, 2013). The NPIC comprises a network of ground

infrastructure to track GNSS. From this network, nationwide corrections to GNSS signals can

be calculated and delivered through the Australian SBAS. These two projects aim to deliver a

national, high-accuracy positioning service to a range of users across Australia.

There are various industries that are potential users of PNT products. For example, users in

civil construction, precision agriculture and rail sectors have growing markets and increasing

use of automated technologies that rely on high-accuracy GNSS positioning. The industry

expectations for positioning requirements such as accuracy, integrity, coverage and

availability, while they exist for some sectors, have not yet been defined in these industries.

Additional considerations like connectivity, interoperability and robustness are increasingly

relevant in the context of automation. These requirements need to be collected and presented

in a uniform format to identify the key applications serviced by future positioning products in

the context of the Positioning Australia programme.

Given Australia’s vast landmass size and population distribution, it is essential to design

services, based on the Positioning Australia programme, that make optimal use of

infrastructure, products, communication channels or delivery methods and business models.

To ensure adoption of these services, it is also essential to understand the potential industrial

uses of precise positioning to help design and deliver efficient, fit-for-purpose and cost-

effective solutions that achieve end-user technical requirements.

The industry requirements for national positioning service in Australia can also be applied to

other countries in the Asia–Pacific region. Collaboration between governments can expand the

benefits to other countries within the coverage of new GNSS services. Further, public access

to services like PPP benefits industrial applications where positioning infrastructure is not

readily available or is cost-prohibitive.

This research aims to study the positioning requirements of applications in the construction,

agriculture and rail industries. The study will review the technical requirements, identify

barriers to adoption and address capability gaps in current positioning services. This study will

serve to inform the planning of a Positioning Australia programme providing a national, real-

time and precise positioning service.

1.3 RESEARCH QUESTIONS

The focus of this study is high-accuracy professional applications like automated positioning

and navigation in the civil construction, precision agriculture and rail industry sectors. The

three research questions are:

1. What is a national high-accuracy GNSS positioning service?

2. What are the current gaps in infrastructure and services for a GNSS positioning service?

3. What are the user requirements for professional applications in civil construction,

precision agriculture and rail?

1.4 PROJECT OUTCOMES

The outcomes to be generated include a comprehensive review of GNSS positioning services,

a requirements analysis and case study validation of applications in civil construction,

agriculture and rail industries.

Outcome 1. A comprehensive review of current GNSS positioning services, industry standards

and requirements within the Positioning Australia context.

Outcome 2. A matrix of functional requirements for key end-user applications in the civil

construction, precision agriculture and rail industries.

Outcome 3. Case studies in the form of validation of end-user application requirements in civil

construction, precision agriculture and rail.

1.5 THESIS OUTLINE

This master’s thesis presents the study in four phases, as shown in Figure 1-1. Each phase is

subdivided into chapters with a total of seven chapters.

Figure 1-1 Research project outline

The first phase outlines background concepts and presents a market analysis of the positioning

services available in Australia. The second phase presents a requirement analysis for the

selected industry sectors. The third phase presents results from case studies for selected

applications. The fourth phase finalises the work with a summary of research findings and

conclusions.

Phase 1: Literature review and market analysis

The first phase of the study involved a literature review in providing a background and

overview of positioning techniques. An analysis of positioning services currently available in

the market will inform how requirements are currently met and, at the same time, identify

capability gaps.

Chapter 2: Positioning and navigation methods

This chapter discusses the basic concept of positioning techniques with a focus on high-

accuracy GNSS. The two techniques studied for this research are RTK and PPP. Current

research topics on RTK and PPP are reviewed and presented to establish the theoretical

framework and highlight the technical characteristics of these positioning techniques.

Current and proposed methods of delivery channels are reviewed and compared.

Chapter 3: PNT infrastructure

This chapter navigates from the global geodetic infrastructure to regional and local

positioning infrastructure used in civil construction, agriculture and rail in Australia.

Current infrastructure projects from government institutions and private industry are

addressed.

Chapter 4: GNSS augmentation services for professional high-accuracy applications

Numerous existing GNSS augmentation services support high-accuracy applications.

This chapter reviews the different services available in the market to understand their

performance and characteristics better.

Phase 2: Requirements analysis

The requirements analysis gathered available criteria, standard metrics and requirements for

several PNT sectors. The data were collected from a comprehensive literature review and

current industry practices from the civil construction, precision agriculture and rail sectors. The

aim was to deliver a requirements matrix specifically for applications in civil construction,

mining, precision agriculture and rail industries with a focus on automation in professional

sectors.

Chapter 5: PNT requirements for professional high-accuracy applications

This chapter reviews the technical requirements that define the performance of PNT

services: accuracy, integrity, coverage, continuity and availability. Additionally, it

presents a requirements framework developed from related industries and publications.

Finally, it proposes a matrix of functional requirements for civil construction, precision

agriculture and rail-based gathered from publicly available information.

Phase 3: Case studies

This final phase focused on the design of case studies for three applications in civil

construction, precision agriculture and rail industries, where the required performance criteria

were validated.

Chapter 6: Evaluation of PNT services for professional high-accuracy applications

With the proposed positioning services and user requirements tabulated, this chapter

reports on field tests designed to demonstrate end-user high-accuracy applications in

civil, agriculture and rail.

Conclusions and recommendations

The conclusions and recommendations chapter finalise the project with findings from the

research and recommendations for future work.

Chapter 7: Conclusions and recommendations

The final chapter presents the findings of the requirements analysis, applications and

validations for civil construction, precision agriculture and rail applications. It also

provides recommendations for implementing services in the context of the Positioning

Australia programme.

CHAPTER 2: POSITIONING AND NAVIGATION

METHODS

2 POSITIONING AND NAVIGATION METHODS

This chapter begins with an introduction to general PNT concepts, in Section 2.1. The focus of

this research is on GNSS as a PNT method, so a description of various satellite-based systems

is presented in Section 2.2. The chapter then introduces the GNSS augmentation methods

available for end-users, in Section 2.3. A discussion of GNSS trends and complementary PNT

technologies follows in Section 2.4.

2.1 OVERVIEW OF POSITIONING AND NAVIGATION

The term navigation refers to the determination of an object’s trajectory based on three

characteristics of the object: position, velocity and attitude. A related term, guidance, refers to

the act of determining the change to an object’s trajectory in relation to its environment required

for it to follow a defined path or reach a point (Hofmann-Wellenhof et al., 2003).

The origin of a coordinate reference frame (Section 3.1) first needs to be fixed to determine an

object’s position and motion over time. Measurements with respect to the reference frame are

used to compute instantaneous position vectors, in the case of position fixing, or trajectory

vectors, in the case of dead reckoning.

Position fixing determines an object’s absolute position by measuring distances from fixed

reference points. Examples of position fixing are pseudorange or range rate measurements, as

used in satellite positioning (Section 2.3). Dead reckoning determines an object’s current

position based on knowledge of its previous position and measurements of its motion.

Examples of dead reckoning are acceleration measurements as used in inertial navigation

(Section 2.4).

It is essential to distinguish the advantages of position fixing and dead reckoning. Satellite-

based positioning is the most practical way of providing position fixing in an absolute reference

frame. GNSS-complementary sensors like IMUs, LiDAR and 3D vision are useful for

providing relative positioning. Both measurement principles are necessary to provide a full

representation of an object’s state and are increasingly used in combination. The following

sections describe GNSS positioning methods and the trends leading to sensor integration.

2.2 SATELLITE-BASED POSITIONING AND NAVIGATION

Satellite-based positioning and navigation was introduced in the 1960s and remains today the

most widely available method for providing users with PNT information through GNSS. The

current GNSS design enables global coverage and instantaneous position, velocity and time

determination in a common reference system. This section presents an overview of the

segments of GNSS design that are evolving into the next generation of services for end-users.

2.2.1 GNSS

The first GNSS was the US GPS, developed in 1973 by the US Department of Defense (DoD)

for military use. As a response, Russia developed GLONASS in 1976 for its own military use.

Both systems consist of a constellation of 24 operational satellites in medium Earth orbit

(MEO) that cover the Earth in different orbital planes. This design provides independent PNT

for users with metre-level precision.

Following the introduction of GPS and GLONASS, China began planning its regional

navigation system in the 1980s, named BeiDou-1, which later evolved into the BDS global

system. BDS includes additional orbital planes in geostationary Earth orbit (GEO) and inclined

geosynchronous orbit (IGSO) to cover the high-latitude regions of the Chinese region. In

Europe, the European Commission (EC) introduced the Galileo programme in 1994 to mitigate

against reliance on the three primary military systems and was the first body to offer an entirely

civilian and commercial service to users.

The system architecture of GNSS involves three components. The space segment is made up

of the satellites and signals. The control segment is made up of a ground network of master and

control stations that track and upload data to satellites. The user segment includes the end-users

in military or civilian applications. Detailed information on system history and architecture for

GPS, GLONASS, BeiDou and Galileo can be found in the literature (Falcone et al., 2017;

Hegarty, 2017; Revnivykh et al., 2017; Yang et al., 2017).

As of March 2020, there were 134 operational GNSS satellites in orbit; 31 GPS, 24 GLONASS,

53 BeiDou and 26 Galileo. Additional satellite systems such as regional navigation satellite

systems (RNSS) and SBAS have been launched to increase visibility, accuracy and integrity in

regional areas. Further research and development propose the use of low Earth orbit (LEO)

communication systems such as Iridium and Globalstar to improve existing GNSS services

(Meng et al., 2018; Reid et al., 2018). Figure 2-1 provides an overview of the current GNSS

IGSO

LEO

GPS GLONASS Galileo BeiDou SBAS QZSS Iridium

GEO

MEO

Figure 2-1 Orbital planes of GNSS. Adapted from Reid et al. (2013)

constellations in their orbital planes.

A significant characteristic of GNSS design following GPS has been frequency

interoperability, meaning that the Galileo and BeiDou signals were designed from the outset to

operate in a similar frequency band and modulation as GPS. This interoperability allows user

hardware to receive multiple GNSS signals with few design constraints in terms of size and

power.

The current frequency plan for GNSS includes different signals on L-band, as shown in Figure

2-2 (Navipedia, 2014). Initially, the use of two frequencies, L1 and L2, in GPS and GLONASS

was deemed necessary to eliminate the effects of ionospheric refraction as a significant source

of error in satellite navigation. When Galileo and BeiDou were planned, a third frequency on

L5 was incorporated into the new systems and as part of a signal modernisation programme to

GPS. A fourth frequency on L6 has also been added to QZSS, Galileo and BeiDou but is not

planned for GPS in the current modernisation programme. L6 includes augmentation services

on new generation GNSS, as discussed in Section 2.4.

The L5/E5/B2 frequency is a significant development for users, and it presents several

advantages. L5 is situated in the Aeronautical Radionavigation Service frequency band, which

is protected from interference by the International Telecommunications Union (ITU). Also, the

design of L5 includes two components: a quadrature-phase (Q) pilot signal and a modulated

in-phase (I) signal. The combination of these components, along with their higher power and

chipping rate, provide better signal tracking compared to previous signals. Finally, L5 is the

third civilian frequency, allowing for dual L1+L5 and triple L1+L2+L5 frequency

combinations for position estimation. A description of signal types is presented by International

Figure 2-2 Frequency bands for GNSS and RNSS (Navipedia, 2014)

GNSS Service (2018).

GPS

GPS is a satellite-based radionavigation system that provides users, primarily military, with

PNT services. The space segment is made up of a minimum of 24 operational satellites

deployed in six evenly spaced orbital planes in MEO. However, more satellites can be assigned

to orbital slots as spares.

The GPS satellites use atomic clocks to precisely control the signal-generating components.

From a fundamental frequency of 10.23 MHz, three carrier frequencies (L1, L2 and L5) are

generated, and different modulation codes are applied to create the signals and services shown

in Table 2-1. There has been a modernisation of GNSS satellites and signals, with four

generations of satellite blocks and new signals incorporated. The M code and L2C signals have

been included in the generation of Block IIR-M satellites launched since 2005. The L2C signal

is divided into two components, a Civil Moderate (CM) and Civil Long (CL) code. The L1C

and L5 signals became operational with the Block IIF satellites in 2017 (GPS Directorate,

2019a, 2019b, 2019c).

The primary services offered by GPS are an open Standard Positioning Service (SPS) for

civilian use and an authorised Precise Positioning Service (PPS) for military use. The specified

accuracy of the GPS SPS is 9 m horizontal and 15 m vertical at 95% (DoD, 2008), although in

practice accuracies of around 3 m horizontal and 5 m vertical are usually observed (Applied

Table 2-1 GPS signals and services overview

Research Laboratories, The University of Texas at Austin, 2019).

Satellite block

Band Frequency (MHz) Signal*

Access

Service

I/II/IIA/IIR

IIR-M

IIF

III

P(Y)

C/A

Authorised PPS SPS Open

1575.42

L1C

P(Y)

SPS Open Authorised PPS Authorised PPS SPS Open

L2CM

1227.60

L2CL

Open SPS Authorised PPS SPS

Open

1176.45

SPS

Open

*P(Y): Precision code, C/A: Civilian Acquisition. C: Civil, M: Military, I: In-phase, Q: Quadrature-phase.

GLONASS

GLONASS was developed as the Russian counterpart to GPS for military and civilian use. The

space segment is similar in having 24 operational satellites but differs in its orbital parameters

and orbital planes, which provide better coverage for high-latitude regions of the Russian

territory.

GLONASS signals currently differ from those of GPS in the signal division method. GPS uses

code-division multiple access (CDMA); that is, every satellite transmits the same frequency

with a pseudo-random noise code unique to each satellite. GLONASS, conversely, uses

frequency division multiple access (FDMA), where each satellite transmits the same pseudo-

random noise codes on its own unique frequency (L1 or L2). In a step towards interoperability,

the GLONASS modernisation programme incorporated a CDMA L3 frequency into its

GLONASS-K1 satellites launched since 2011. Additional CDMA signals for L1, L2 and L3

were added to the new generation of GLONASS-K2 satellites launched in 2019. Table 2-2

provides a summary of GLONASS signals and services.

Similar to GPS, there are two primary services, an open public service and an authorised

military service. The performance of the civilian Open Service (OS), as measured by the signal-

in-space range error (SISRE), is currently in the range of 1–2 m. Future system improvements

led by the GLONASS Performance Improvement Plan promise to achieve a SISRE of 0.5 m by

Table 2-2 GLONASS signals and services overview

2020 (Druzhin & Palchikov, 2014).

Satellite block

Band

Signal*

Access

Service

Frequency (MHz)

Multiplexing mode

G G-M G-K1 G-K2 G-V

FDMA

L1OF

Open

SPS

X X

1602 + 0.5625*k

FDMA

L1SF

Authorised

PPS

X X

CDMA

L1OC

Open

SPS

1600.995

CDMA

L1SC

Authorised

PPS

FDMA

L2OF

Open

SPS

X X

1246 + 0.4375*k

FDMA

L2SF

Authorised

PPS

CDMA

L2OC

Open

SPS

1248.06

CDMA

L2SC

Authorised

PPS

CDMA

L3OC

Open

SPS

1202.025

L3SC

Authorised

PPS

CDMA *O: Open, S: Authorised Special, F: FDMA, C: CDMA

BeiDou

BeiDou, formerly known as COMPASS, is the Chinese GNSS programme and the third

independent GNSS in operation. In 1994, BeiDou started as a demonstration phase named

BDS-1; later, a regional system termed BDS-2 was deemed operational in 2012 around the

Asia–Pacific region. The next phase, named BDS-3, is currently in development and will

provide global coverage by the end of 2020. As the constellation of BeiDou initially had a

focus on regional coverage, a unique combination of multiple orbits was used: MEO, GEO and

IGSO.

BeiDou-3 transmits on three frequencies (B1, B2, B3) with four signals modulated as part of

an OS and three signals used for an authorised service, while additional services like short

message communication service (SMCS), search and rescue (SAR) and PPP are modulated in

the B2b signal (Table 2-3). BeiDou incorporates an SBAS as part of its system design, unlike

GPS, GLONASS and Galileo, which operate their augmentation systems as complementary

architecture. Three GEO satellites transmit the SBAS and PPP services for regional

augmentation.

The BeiDou OS specifications for SISRE at 95% are 2.5 m horizontal and 10 m vertical for the

regional coverage area, while the specified user range error at 95% is 10 m for horizontal

(China Satellite Navigation Office, 2016). Test results on the user range error have been

measured at around 3.5 m horizontal at 95%, and this is expected to improve when the system

Table 2-3 BeiDou-3 signals and services overview

is fully operational (Yang et al., 2017).

Band

Access

Service

Frequency (MHz) 1561.098

Signal* B1I

Open

SPS

Open

SPS, SBAS

B1C

1575.42

Authorised

SPS

B1A

1176.45

Open

SPS, SBAS

B2a

1207.14

Regional

PPP, SAR, SMCS

B2b

Open

SPS

B3I

Authorised

SPS

B3Q

1268.52

Authorised

SPS

B3A

*I: In-phase, Q: Quadrature-phase. A: Authorised, B1C is interoperable with GPS L1C, B2a/B2b are subdivided

components of the B2 signal.

Galileo

Galileo is the European Union’s GNSS and was developed as a system independent of GPS,

GLONASS and BeiDou. The motivation behind the programme is unique in that it does not

serve a military purpose but is instead focused on civilian and commercial requirements. When

fully deployed the constellation will involve 30 satellites (24 operational and 6 spares) in MEO.

The frequency design of Galileo includes three frequencies: E1, E5 and E6. There are three

primary levels of positioning service. The OS provides open access PNT services to the public.

The Public Regulated Service (PRS) incorporates encryption to government-authorised users

that require a high level of service continuity. Finally, the High-Accuracy Service (HAS)

transmits PPP corrections with decimetre-level accuracy.

There are additional services. The Safety-of-Life (SoL) service provides integrity information

for safety-critical applications such as rail and aviation. The SAR service includes a return link

channel to send distress signals from receivers with beacons to Galileo satellites. Navigation

Message Authentication (NMA) adds a digital signature to the OS to ensure data authenticity,

and the commercial authentication service adds signal encryption to protect against spoofing

attacks. Full details of Galileo signals and services are specified by European Union (2016,

2019) and are summarised in Table 2-4.

Galileo has specified performance requirements for the OS and the PRS in both single-

frequency and dual-frequency modes. For the OS, single-frequency accuracy at 95% is 15 m

horizontal and 35 m vertical, while dual-frequency accuracy at 95% is 4 m horizontal and 8 m

vertical. For the PRS, dual-frequency accuracy at 95% is 6.5 m horizontal and 12 m vertical.

At the time of writing, performance requirements had not yet been published for the Galileo

HAS, but it aims to deliver open access performance at the decimetre level (Fernández-

Table 2-4 Galileo signals and services overview

Hernández et al., 2018).

Band Frequency (MHz) Signal* Access

Service OS

Open

E1-B

1575.42

Authorised PRS

E1-C

Open

E5a-I

1176.45

Open

E5a-Q

Open

E5b-I

1207.14

Open

E5b-Q

Open

E6-B

HAS

1278.75

E6-C

Authorised NMA

*I: In-phase, Q: Quadrature-phase. B: data component, C: pilot component, E5a/E5b are subdivided components

of the E5 signal.

System tests during the initial validation phase with four satellites achieved a SISRE of 1.11 m

horizontal at 95%. More recent tests in 2016 achieved end-user accuracies of less than 5 m

horizontal at 95% (Falcone et al., 2017).

2.2.2 RNSS

In addition to the four GNSS, several countries have developed their own regional systems to

improve visibility, integrity and other performance parameters. There are currently two RNSS

in operation: Japan’s QZSS and the IRNSS, also called Navigation with Indian Constellation

(NavIC). Each optimises coverage of its specific region of the Earth by using a particular type

of orbit.

A GEO is a circular, geosynchronous and equatorial orbit, so satellites appear at a fixed position

in the sky. An IGSO is a circular, geosynchronous orbit with an inclination from the equator

so that satellites can appear at higher altitudes than those on GEO. IGSO orbits are used for

higher-latitude regions. A highly elliptical orbit is an elliptical, high-eccentricity orbit that

makes satellites remain at high altitudes in a small region for an extended period than those on

IGSO.

QZSS

QZSS is the Japanese regional navigation system proposed in 2002 to complement and

augment GPS for the Asia–Pacific region. The initial plan for three satellites in an IGSO orbit

and one GEO would ensure at least one satellite was visible at a high zenith angle for Japan.

This high zenith angle increases availability in built-up urban areas where buildings obstruct

views from low-orbiting satellites. The four-satellite constellation was deemed operational in

2018, and an upgrade to a seven-satellite constellation is planned for operation in 2023.

The QZSS satellites transmit four signals on three frequencies (L1, L2, L5) designed to be fully

interoperable with GPS. An additional two signals are transmitted on a frequency (L6) that

provides augmentation services for code and carrier phase positioning. A summary of signals

and services is provided in Table 2-5 (Kogure et al., 2017). The QZSS positioning services are:

• GPS Complementary Service: transmits GPS-interoperable signals for increased

availability

• Sub-metre-level Augmentation Service (SLAS): provides augmentation to code

positioning

• Centimetre-level Augmentation Service (CLAS): provides augmentation to carrier phase

positioning

• PRS: service for authorised users with encryption-based security.

The QZS-1 satellite transmitted an L-band experimental (LEX) signal during an initial testing

and validation phase. Some research was carried out in Australia to explore extending the

capability of the QZSS augmentation message beyond Japan (Harima et al., 2017). As QZSS

moved into its operational phase, the signal is now officially termed the CLAS signal for Japan

and the Multi-GNSS Advanced Demonstration Tool for Orbit and Clock Analysis (MADOCA)

signal for regional coverage. One of the main characteristics of this signal, in contrast to other

GNSS, is the high data-transmission rate of 2000 bps realised through code shift keying

modulation.

Additional services offered by QZSS are an early warning service broadcast in case of natural

disasters, and a message communication service where short messages can be sent through a

communication link as a confirmation between users during natural disasters.

The performance of QZSS is specified by a SISRE at 95% of 2.6 m horizontal while test results

have shown a root mean square (RMS) value of 0.6 m (Kogure et al., 2017). The specified

performance for the CLAS service at 95% is 12 cm horizontal and 24 cm vertical in kinematic

Table 2-5 QZSS signals and services overview

mode (Government of Japan, 2018).

Band Frequency (MHz)

Signal

Access

Service

L1-C/A Open

GPS interoperable

L1C

Open

GPS interoperable

1575.42

L1S

Open

SLAS, SMS

L1Sb

Open

L1 SBAS

1277.60

L2C

Open

GPS interoperable

Open

GPS interoperable

1176.45

L5S

Restricted L5 SBAS (DFMC*)

L6D

Open

CLAS

1278.75

L6E

Restricted MADOCA

*DFMC: Dual frequencies and multiple constellations. L1C/L2C are compatible with GPS L1C/L2C, S, Sb, D

and E are signal components.

IRNSS/NavIC

The IRNSS, or NavIC, is an independent regional system developed by the Indian Space

Research Organisation to meet PNT needs in the Indian region. Coverage is ensured by three

GEO satellites and four IGSO with increased coverage in the region.

The IRNSS transmits two frequencies on L5 and S-band, chosen because the L1 and L2 bands

used by RNSS are entirely utilised by existing GNSS. The S-band frequency, initially reserved

for radio determination satellite services, was approved by the ITU for use in GNSS (Kogure

et al., 2017).

IRNSS provides an SPS that is open to civilian users, and a Restricted Service (RS), which is

encrypted and available to authorised users. A summary of the signal plan and services is

Table 2-6 IRNSS signals and services overview

provided in Table 2-6.

Band Frequency (MHz)

Signal

Access

Service SPS

L5-SPS Open

1176.45

L5-RS

S-SPS

2492.028

S-RS

Restricted RS SPS Open Restricted RS

IRNSS is designed to offer horizontal accuracy of 20 m, but tests carried out in 2015, shortly

after the fourth satellite was launched, demonstrated position fix accuracies of 10 m or better

(Ganeshan et al., 2015).

SBAS

The design of SBAS involves an independent constellation of GEO satellites transmitting

additional information that improves the performance parameters of GNSS within a specified

service area. The first proposal for a public SBAS was developed in 1994 by the US Federal

Aviation Administration. Having realised the potential of augmenting aircraft navigation with

a high-integrity system based on GPS, the Wide-Area Augmentation System (WAAS) was

launched in 2003 for North America.

The International Civil Aviation Organization (ICAO) simultaneously developed standards for

the interoperability of different SBAS for use in other regions. The Multi-functional Satellite

Augmentation System (MSAS) in Japan, the European Geostationary Navigation Overlay

Service (EGNOS) in Europe and the GPS-aided GEO Augmented Navigation (GAGAN)

system in India were developed to be compatible with GPS. The Russian System for

Differential Corrections and Monitoring (SDCM) was launched in 2011 to be compatible with

GLONASS. The Chinese BeiDou Satellite-based Augmentation System (BDSBAS) is unique

in that it was designed to be integrated into the regional BDS-1 system since its launch in 2003.

However, ICAO certification has not yet been granted to BDSBAS. More details on SBAS

constellations are presented in Walter (2017).

Other regions are also planning on developing future SBAS to implement the ICAO standard.

Korea has announced an SBAS for their region Korean Augmentation Satellite System

(KASS). Australia and New Zealand entered a trial of a second-generation SBAS in 2017.

Proposals for an African SBAS called African Satellite Augmentation System (ASAS) and a

South American SBAS named Sistema de Aumentación para el Caribe, Centro y Sudamérica

(SACCSA) are in the feasibility assessment stage (Walter, 2017). The current state of global

SBAS coverage is shown in Figure 2-3.

Second-generation SBAS include the capability of transmitting corrections on DFMC. One of

these systems is being demonstrated in the Australian SBAS Testbed, and future upgrades to

WAAS and EGNOS include dual-frequency (L1+L5) and integration of multiple constellations

as part of their development plans.

Figure 2-3 Current SBAS service areas (GSA, 2018a).

Clarification must be provided regarding the use of the term SBAS. The systems discussed here

are ‘aviation-style’ SBAS because they focus on certified SoL applications in aviation. There

are additional commercial systems that operate according to the same technical principle but

do not have the certification to be used in aviation applications. Examples of such commercial

systems are OmniSTAR, StarFire, TerraStar and Atlas. These commercial services are

discussed in Chapter 4.

2.3 GNSS AUGMENTATION METHODS

Using the GPS’s SPS or equivalent from other GNSS providers allows for a range of navigation

applications with relatively low accuracy and integrity because of errors inherent in the position

estimation. For applications where improved navigation performance is required, real-time

augmentation systems are implemented to enhance accuracy, integrity, availability and

reliability.

GNSS augmentation methods are classified here in two ways. Classification by observable

type—that is, code and carrier phase—differentiates on the basis of accuracy. These methods

are described in Section 2.3.1. Classification by representation space provides a distinction on

the basis of coverage area. These methods are described in Section 2.3.2.

The communication links to deliver these augmented services can also be classified as ground-

based data links, satellite-based data links or Internet delivery (Lachapelle et al., 2010). These

are described in more detail in Section 2.3.3.

2.3.1 GNSS Observables

Augmentation methods are described as any GNSS measurement technique using additional

correction sources to improve navigation parameters of the SPS. GNSS augmentation methods

can be classified according to their measured signal or observable components. It is essential

to differentiate between these measurement techniques as they influence receiver design and

accuracy. Code measurements can be used in simple receiver and antenna hardware designs

and allow for sub-metre accuracy. Carrier phase tracking allows for centimetre-level

positioning but at the cost of more sophisticated user hardware and processing algorithms.

Code-based Augmentation: DGNSS and WADGNSS

Code-based augmentation refers to positioning techniques where the pseudorange code

observables are used in conjunction with additional correction information. There are two main

ways of using code corrections: DGNSS and wide-area DGNSS (WADGNSS).

In DGNSS, a single reference station on a known location calculates the errors in the code

observables. It delivers them to a user receiver as a correction message through a real-time data

link. The accuracy of the solution degrades over distance from sub-metre within 100 km to

several metres with increasing distance from the base station. End-user devices can be low-

cost, single-frequency code receivers. However, some receivers can include L1 carrier phase

tracking to improve positioning via the use of a measurement technique called carrier phase

smoothed pseudorange.

WADGNSS is a differential method that combines satellite orbits and clocks corrections that

model errors over a large area. An example of this is in SBAS systems that can provide metre-

level accuracies by transmitting corrections over a satellite-based data link or the Internet

(Lacarra et al., 2013; Raman & Garin, 2005).

Phase-based Augmentation: RTK, PPP and SSR-RTK

Phase-based augmentation includes positioning techniques where the carrier phase observables

are used together with correction information transmitted to the user. Carrier phase

observations are used for RTK, NRTK, PPP, PPP with ambiguity resolution (PPP-AR),

ionosphere-assisted PPP (PPP+Ion) and RTK based on state-space representation (SSR-RTK).

RTK is a relative positioning technique in which carrier phase observation messages are

delivered to a user to calculate corrections. By using carrier phase measurements, it is possible

to obtain position fixes at the centimetre level with an operational range of around 10 km for

single-frequency observations (L1) and 20 km for dual-frequency observations (L1/L2). With

triple-frequency observations, it is possible to increase the accuracy and range to around

100 km (Li, 2018).

An extension of the RTK method is NRTK, which relies on Internet-delivered corrections from

a network of Continuously Operated Reference Stations (CORS) (Janssen et al., 2012). This

approach models the measurement errors from several CORS into a combined solution around

the user. NRTK increases the effective baseline distance to around 50 km, yet still allows for

accurate solutions.

One limitation of RTK and NRTK is their dependence on real-time communication links. The

most cost-effective and commonly used delivery method for RTK is ground radio, which has

a range of 1–20 km depending on terrain and frequency. The Radio Technical Commission for

Maritime Services (RTCM) has standardised transmission formats that are now compatible

with all RTK receivers.

For NRTK, it is necessary to use a two-way communication channel such as mobile Internet.

This delivery method has greater coverage and has grown in popularity together with CORS

networks. The transmission of RTK corrections over the Internet is achieved using the

Networked Transport of RTCM via Internet Protocol (NTRIP), which is now standard on most

GNSS receiver hardware. It is possible today to deliver RTK corrections from any reference

receiver using NTRIP. However, this will only work in areas with mobile Internet.

The accuracy at the 95% confidence interval of RTK is around 25 mm horizontal and 35 mm

vertical for a 5-km baseline. As GNSS measurement errors are spatially correlated, the

accuracy degrades by 1 ppm for RTK and 0.5 mm for NRTK. It is essential, therefore, to

consider the distance to base or NRTK CORS density for high-accuracy applications.

The PPP approach to GNSS positioning was initially proposed as an efficient method to process

large numbers of global stations. PPP consists of correcting satellite and receiver specific errors

and modelling atmospheric and geophysical effects to derive a float solution with decimetre

accuracy (Kouba & Héroux, 2001; Zumberge et al., 1997). This approach can provide

consistent accuracy over much greater distances than the relative positioning model but at the

cost of longer initialisation times, which makes it impractical for real-time applications

(Bossler et al., 2010).

The initialisation times required for the solution to converge on a steady-state, decimetre float

solution can range from 30 minutes to multiple hours (Seepersad, 2012), so its applications are

limited. However, commercial PPP providers have focused their real-time services on marine

and agriculture applications with remote, open sky environments, where convergence and re-

initialisation times are a lesser constraint. There are also several research-focused PPP data

products delivered over the Internet using multi-GNSS and open formats from the Japan

Aerospace Exploration Agency (JAXA) and the International GNSS Service (IGS) (JAXA,

2015; Kouba, 2009; Mervart et al., 2008).

The combination of PPP with ambiguity resolution (AR) and further with RTK has been

proposed to improve real-time accuracy and convergence times (Wübbena et al., 2005). Real-

time PPP services and their AR techniques were reviewed by Grinter and Roberts (2013). Some

commercial services have implemented a combined PPP and RTK approach over regional areas

(Leandro et al., 2011), and further performance improvements are currently under

investigation. In Australia, research is underway on the integration of PPP and RTK to take

advantage of both wide-area coverage and rapid AR (Teunissen & Khodabandeh, 2015; Choy

& Harima, 2019) delivered through the Positioning Australia programme (Geoscience

Australia, 2019a).

2.3.2 GNSS Representation Methods

GNSS augmentation methods improve the positioning accuracy of a user by modelling errors

using two approaches or representation spaces. The observation-space representation (OSR)

approach models the errors in a local area while the state-space representation (SSR) approach

models errors that can be applied to a global coverage area. Figure 2-4 shows the representation

space domains of error corrections in their OSR and SSR classification.

The OSR model is applied to DGNSS, RTK and NRTK techniques. In the OSR approach,

range estimates from error sources in the GNSS code and phase observations are computed

from a reference base station as a ‘lump-sum’ correction transmitted to the user.

The OSR solution is relative to the base station reference frame, and its accuracy is baseline

dependent. This solution is limited to an effective local or regional coverage and cannot be

scaled across global coverage. However, the OSR approach gives the best performance for

high-accuracy and real-time users. The accuracy of RTK, in particular, is the highest achieved

Figure 2-4 Spatial representation of corrections. Adapted from Lachapelle et al. (2010)

In contrast, the SSR approach computes the individual error sources from orbits, clocks, signal

with any augmented GNSS technique, in the order of a few centimetres.

biases and atmospheric delays as components of a state vector. PPP, PPP-AR, PPP+Ion and

SSR-RTK are examples of augmented GNSS solutions based on the SSR model. The individual

Table 2-7 Corrections used for different SSR solutions. Adapted from Choy & Harima (2019)

corrections used for each of these solutions is shown in Table 2-7.

Correction

SSR-RTK

Satellite orbits

Satellite clocks

Code biases

PPP

Phase biases

Ionospheric delay

PPP-AR

PPP+Ion

Tropospheric delay

With PPP, computing the individual magnitude of orbit and clock errors provides positioning

in the reference frame of the GNSS orbits. Thus, the solution is independent of the reference

station distance. SSR solutions can be applied on a global scale. In practical terms, for a country

the size of Australia, a national high-accuracy positioning service will need to deliver SSR

corrections that are scalable and applicable across its vast area.

The main limitation of the PPP method is that its position estimation requires a relatively long

time to converge to an accurate solution. The accuracy of this PPP float solution, once it reaches

a steady-state, is at the decimetre level. However, convergence time can be improved by adding

code and phase biases corrections to the estimation. This enables a PPP-AR solution, which

achieves decimetre accuracy in a shorter time.

The convergence time and accuracy can be further improved by adding corrections to

atmospheric delays from dense RTK networks. A commonly used term for this approach is

PPP based on RTK networks (PPP-RTK) or SSR corrections. However, the improvements are

only achieved within the regional area of the RTK network, which makes this approach similar

to NRTK in terms of coverage. Hence, the better suited term of SSR-RTK has been described

by Choy, Bisnath & Rizos (2017) and Choy & Harima (2019) and is used throughout this

research.

There are two types of atmospheric corrections that can be generated from RTK networks:

ionospheric and tropospheric delays. Solutions, such as the PPP+Ion developed by Harima et

al. (2015), use corrections to ionospheric delay only. SSR-RTK solutions make use of

corrections to all the individual error sources.

Other combinations of OSR and SSR techniques are possible. Users of NRTK services,

dependent on mobile Internet, can fall back on satellite-delivered PPP solutions when Internet

connectivity drops out. These mixed processing and delivery techniques are examples of

industry trends providing users with higher accuracy and greater coverage.

The coverage of a PNT solution combines the functional range of the positioning technique

and the operational range of the delivery channel. For example, Internet-delivered RTK

solutions are functional within 50 km of a CORS; however, in areas outside of mobile Internet

range, users will not have operational coverage. Coverage of satellite-delivered PPP is global

in both functional and operational terms. It is necessary to combine the coverage of satellite-

delivered PPP with the accuracy of RTK to provide a homogeneous SSR-RTK solution across

a vast regional expanse like Australia.

The relationship between accuracy and coverage is also essential because these are two of the

primary user requirements and characteristics of augmented GNSS. Figure 2-5 shows a

simplified arrangement of augmentation methods, ranging from global coverage and ‘standard

e d o C

e s a h P

Figure 2-5 GNSS augmentation methods organised by accuracy and range. Adapted from NovAtel Inc. (2017)

accuracy’ services like the GNSS SPS to local coverage and ‘high accuracy’ services like RTK.

2.3.3 GNSS Augmentation Delivery Channels

The corrections generated in PPP and RTK can be delivered over multiple channels. An

example of this is current commercial services offering their wide area and PPP augmentation

solution via a mix of satellite L-band and Internet. This section describes the available data

links for corrections, ground-based data links, satellite-based data links and Internet delivery.

Ground-based Data Link

Legacy networks of dedicated radio beacon towers delivering GNSS augmentation in the low

frequency (LF), medium frequency (MF) and very high frequency bands have traditionally

been used as data links for DGNSS navigation in maritime and aviation applications. The

Australian Maritime Safety Authority operates a DGNSS service along the country’s coasts

and ports in the LF and MF band (285–325 kHz) (Pugsley, 2016). In aviation, a ground-based

augmentation system (GBAS) or local area augmentation system for CAT I approaches has

operated at Sydney Airport since 2014 and is being considered for extension to other airports

(Airservices Australia, 2015). This type of DGNSS augmentation provides increased accuracy,

integrity monitoring and reliability using industry-standard Radio Technical Commission for

Aeronautics and RTCM data formats. The range of the radio link and thus the coverage of the

augmentation data link stretches to around 300 km for LF.

For higher-accuracy applications using RTK augmentation data links, the current standard is

the licenced ultra-high frequency (UHF) band in the 450–470 MHz range. As of February

2020, the Australian Communications and Media Authority (ACMA) Register of

Radiocommunications Licences showed over 24,800 licences for this band in Australia

(ACMA, 2020) and managing these frequency assignments and interference becomes a

problem in densely populated areas.

The transmission range of UHF is generally limited to around 10 km in optimal conditions but,

in practice, a range of a few kilometres can be expected around rough terrain and vegetation

cover. The range can be extended with the use of radio repeaters, but their operation can be

complicated and time-consuming. Despite its limitations, radio remains the default

transmission method for RTK, and standard radio protocols are well established as compatible

with most GNSS equipment.

Some newer applications such as mobile robotics and UAVs generally have an existing

telemetry link to a user control centre that can be shared for RTK. These links are generally in

the unlicensed spread spectrum bands 915–928 MHz and Wi-Fi 2400–2483.5 MHz, although

they have limited range compared with UHF.

Some professional users must have dedicated communication links that guarantee security and

reliability. Most of the solutions have been through UHF or other ground-based data links and

are preferred where Internet connectivity is limited. However, as connectivity solutions mature,

users can now employ more robust channels like satellite or Internet delivery that are

progressively increasing in coverage.

Satellite-based Data Link

WADGNSS and PPP services require corrections to be broadcast over a vast region. For these

types of augmentation, it is practical to use a dedicated L-band satellite link as the

communication channel. For example, public SBAS relies on satellite-based data links

standardised on the GNSS L1 frequency. This allows any GNSS receiver to decode and be

compatible with SBAS signals easily.

The maritime and offshore industry use Inmarsat satellites for satellite communication in

remote areas. Commercial GNSS service providers began utilising Inmarsat satellites to deliver

WADGNSS and PPP corrections to these industries. The range of applications has expanded

from maritime to agriculture, mapping, survey, construction and automotive industries. The

commercial market for corrections has driven performance improvements into decimetre-level

accuracies and convergence times of several minutes (Banville et al., 2014; Leandro et al.,

2011).

The main advantage of satellite-based data links is increased coverage. SBAS and commercial

providers use multiple GEO satellites that can extend to global coverage. For example, the

Trimble RTX service provides almost global coverage from a network of five GEO Inmarsat

satellites and provides continually improving service performance (Trimble Inc., 2019).

Trimble RTX has been upgrading its delivery to include new signals with bandwidths of 600–

2400 bps and using proprietary compression formats that improve its SSR-RTK services. New

signals offer additional Advanced Encryption Standard (AES) signal encryption transmitted to

selected compatible receivers at the user end (Trimble Inc., 2019).

A different approach to satellite-delivered augmentation relies on the existing L6/E6 frequency

band in some MEO and IGSO GNSS constellations, such as Galileo and QZSS. This enables

the availability of the service to be increased by having additional satellites and can reduce the

hardware requirements on the user side. It is possible for system providers to deliver correction

services directly and with performances matching those of commercial providers (Choy et al.,

2014; Fernández-Hernández et al., 2014).

There are also LEO constellations proposed as redundant or complementary systems to existing

GNSS. LEO-enhanced GNSS is under investigation by system operators such as BeiDou and

commercial satellite communication companies such as Iridium. These systems are not

currently operational, so the GEO and MEO satellite delivery channels are likely to continue

to be used over the coming decade. It is possible that, once LEO constellations are operational,

the satellite delivery model from multiple constellations will be the dominant augmentation

channel from public and commercial operators focused on future emerging applications.

These approaches create a new generation of services from commercial and system providers

that rely on satellite-based data links. Currently, Geoscience Australia is investigating the

implementation of an Australian SBAS that will include the latest generation of services with

the addition of L5 for DFMC SBAS and PPP. For a review of satellite-based data links

currently available for the Australian context, see Choy et al. (2017).

Internet Delivery

As mobile Internet becomes widespread, it is practical to use Internet-delivered GNSS

augmentation without the coverage and bandwidth limitations of traditional ground-based data

links. Currently, Australia has mobile Internet coverage in most built-up areas, but there are

still vast, remote areas with connectivity black spots. This is a characteristic of this country’s

size and population distribution, relative to that of more densely populated countries such as

Japan and Germany.

An additional benefit of Internet delivery is that it allows for a two-way data link, which enables

NRTK solutions from CORS networks. Australia has several CORS networks managed by

government data service providers that distribute DGNSS, RTK and NRTK augmentation

services through a value-added reseller (VAR) network as described by Hausler (2014).

Traditional PPP service providers—those who have access to Inmarsat services—also benefit

from Internet delivery. For example, Trimble delivers its augmentation services through both

channels: L-band and Internet (Leandro et al., 2011), and often uses them as a backup in case

of NRTK outages, in a service called xFill (Krzyżek, 2013).

New commercial providers in the market, particularly those offering PPP services for the

automotive sector, such as Swift Navigation and Sapcorda Services, are starting to deliver PPP

solutions exclusively over the Internet. This is important to consider given that connectivity is

a primary requirement in the automotive sector. If positioning, automation and connectivity are

technological advances driving the automotive sector, the same factors are driving the

professional sectors. It is possible then that connectivity becomes the main requirement of

professional applications, and Internet delivery becomes the most straightforward way of

providing GNSS augmentation.

With increased coverage and availability of mobile Internet, and as delivery channels expand

into different frequency bands including satellite-delivered Internet (Reid et al., 2018), the

classification of data links as ground or space is becoming blurred (Feng et al., 2009). Internet

delivery may become a means of providing a communication link for multiple integrated

services with a seamless mixed-delivery carrier including telemetry, data and GNSS

augmentation.

2.4 GNSS TRENDS AND COMPLEMENTARY POSITIONING TECHNOLOGIES

For industries like robotics, automotive, construction, agriculture and rail, GNSS is one of

many sensors in a stack of positioning sensors. There are key trends that promise to make

GNSS adoption easier for hardware manufacturers. There are also complementary positioning

technologies that increase the robustness and availability of sensor-fused solutions. These

developments are discussed in this section.

2.4.1 GNSS Trends

GPS is currently launching new-generation satellites from its modernisation programme, which

includes improvements to the signal structure, atomic clock accuracy and orbit determination.

These improvements are steadily increasing the accuracy of standalone GPS. One example of

this is the addition of new civilian signals L2C and L1C, which offer better tracking

performance. However, one of the most significant developments for users is the addition of

the public L5 frequency, which will enable dual-frequency positioning (L1+L5) at a lower cost

and better performance than the traditional L1+L2 combination.

The L5 frequency is currently available on 12 GPS Block IIF satellites and future GPS Block

III, and is used by all Galileo and QZSS satellites. In preparation for a fully operational L1+L5

multi-GNSS environment, equipment manufacturers have begun to launch dual-frequency

RTK chips aimed at low-cost mass-markets. These products will potentially unlock high-

accuracy positioning for a broader range of applications (North Coast Media LLC, 2017a).

A fourth frequency on L6 is also being transmitted by QZSS and Galileo, offering high-

accuracy PPP augmentation services for public use. Galileo will provide global coverage while

QZSS’s service area is limited by the constellation’s footprint in the Asia−Pacific region. In

mainland China, BeiDou has similar capabilities and can be expanded to other regions within

the GEO satellites’ footprint. Similarly, the Australian SBAS project is proposing a PPP service

for the Australasian region. It is therefore expected that PPP will be a public service provided

by several operators alongside commercial offerings, which will need to differentiate on

additional features.

One of these features relates to the susceptibility of GNSS to spoofing. Several industries have

addressed these concerns by developing more robust and resilient PNT solutions. Two

approaches, already used by military organisations, are authentication of the navigation

message level and encryption on the signal level. These two features will be available to

professional users through the Galileo HAS on E6. Commercial L-band service providers also

offer authentication for safety-critical applications like automotive and aviation.

Another risk in GNSS is jamming and interference, which can completely block a positioning

system reliant on a GNSS as a single source of positioning. By combining PNT systems that

use different measurement techniques and signal sources, it is possible to mitigate against the

risk of signal attack and increase the robustness and resilience of a positioning solution.

2.4.2 Complementary Positioning Technologies

GNSS is one of the most versatile positioning methods used, and its limitations can be

overcome by complementing the position estimation with other sensing techniques. An

example is to combine different measurement types to derive an object’s position and motion

(from GNSS), its attitude (from an IMU or compass) and its environment (from ultrasonic,

sonar, vision or LiDAR). A description of non-GNSS positioning sensors used for robotics is

presented by Siciliano & Khatib (2016).

The use of different instruments contributes to the position determination in an approach

referred to as multi-sensor data fusion. Each measurement method has its own unique set of

physical limitations, operational range and accuracy. The integration of different instruments

can improve the accuracy and extend the positioning solution into GNSS-denied environments

such as building interiors, mines, tunnels and urban canyons.

One approach is to increase robustness solely on the user hardware, by integrating GNSS with

an inertial navigation system (INS). These systems operate independent of additional

infrastructure and are widely used in train control systems to provide odometry, velocity and

position along a track. Integrated GNSS+INS receivers are available from most GNSS

manufacturers and are replacing traditional INS in use for rail and automotive applications and

in GNSS-denied environments.

Another approach is to build additional positioning infrastructure, such as networks of radio

frequency (RF) beacons, to extend the GNSS solution into obstructed or indoor areas. Some

solutions, like those provided by Locata (Locata Corporation, 2020) and Syntony (Syntony,

2020), are compatible with existing GNSS receivers while others require additional user

hardware. In both cases, users can transition between open sky and indoor environments,

allowing for the possibility obstacle avoidance and automation in robots and UAVs.

2.5 SUMMARY

This chapter has presented an overview of the GNSS positioning techniques, augmentation

methods and delivery channels that make up current high-accuracy positioning services. This

helps to understand what users expect from a GNSS positioning service, as proposed in

research question 1. Some important trends in GNSS and complementary positioning

technologies were also discussed to situate GNSS within the broader context of PNT solutions

and future techniques. Defining these fundamental concepts and classifications is the first step

in understanding what a national GNSS positioning service would look like for the Australian

context.

Several available methods of GNSS augmentation increase performance parameters of GNSS

solutions. DGNSS and WADGNSS are code-based techniques that can achieve sub-metre-

level accuracies, while RTK and PPP are phase-based techniques that achieve centimetre-level

accuracies. The Australian SBAS project proposes both a WADGNSS and a PPP service, but

the current research focuses on high-accuracy (centimetre to decimetre) services like RTK and

PPP. Chapter 5 and 6 will further analyse these positioning methods in different sectors and

applications.

GNSS augmentation methods can be classified into two computational approaches, OSR and

SSR. In terms of coverage, the first is effective within a local or regional area while the second

can be applied globally. In terms of accuracy, OSR techniques like RTK and NRTK provide

the highest accuracy but are dependent on a dense network of CORS and Internet delivery. The

hybrid SSR-RTK correction can be delivered over L-Band but still relies on a dense CORS

network for fast convergence. The impact of GNSS infrastructure is further discussed in

Chapter 3.

Some GNSS system providers including QZSS and Galileo are working on high-accuracy,

open-access PPP services delivered over the existing L6 band. This trend is likely to affect the

marketplace for commercial service providers. This is discussed in Chapter 4 in terms of

creating opportunities for new companies to cater to new user segments. It is essential, then, to

address any potential challenges in infrastructure, market and user requirements so that the

benefits of forthcoming positioning services are maximised for professional users. The

following chapters discuss these topics.

CHAPTER 3: PNT INFRASTRUCTURE

3 PNT INFRASTRUCTURE

The assets required to facilitate the essential functions of a society and economy are considered

critical infrastructure. Given the dependence of critical sectors on GNSS, the US Department

of Homeland Security (DHS) along with department from other countries have proposals in

place to elevate the level of GNSS to an official system dependency for critical infrastructure

(DHS Science and Technology Directorate, 2016). The intention is to increase the protection

and resilience of GNSS.

The combination of physical assets and services required to perform PNT based on dependent-

positioning techniques can be termed PNT infrastructure. This infrastructure is currently used

to define geodetic reference frames in an international, regional or state-based context.

However, it is increasingly vital to provide PNT services to scientific, defence, public or private

sectors. The Positioning Australia programme proposes to maintain and operate the

infrastructure required to deliver services to the public that achieve 3-cm position accuracy.

This chapter discusses GNSS infrastructure and its impact on scientific, public and private

sectors. It also explores the current gaps and additional GNSS infrastructure considerations for

professional applications in the construction, agriculture and rail sectors.

3.1 REFERENCE FRAMES AND COORDINATE SYSTEMS: FROM GLOBAL TO

LOCAL

Coordinate systems are used to define the positions of points in space. Reference frames

incorporate physical markers and measurements to realise the coordinate system in physical

space. In geodesy, global geodetic reference systems have been developed to define the Earth’s

physical shape and attributes, while providing the basis for positioning and navigation at a

global scale. A global reference frame is necessary for the operation of satellites. It is of

increasing importance given society’s reliance on space-based technologies and the integration

of geospatial products across international borders. This reliance has been made evident in the

United Nations General Assembly adoption of a resolution entitled A Global Geodetic

Reference Frame for Sustainable Development (UN-GGIM Global Geodetic Reference Frame

Working Group, 2015).

Global geodetic reference systems and frames are scientific endeavours defined by the

international community, the most widely adopted of which is the International Terrestrial

Reference System and Frame (ITRF) published by the International Association of Geodesy.

The parameters defining this reference frame are the best global approximation to the shifting

shape of the Earth. There are also regional and national reference frames that are better suited

to the characteristics of specific regions. The six divisions tasked with the maintenance of

regional reference frames used by the International Association of Geodesy are the African

Geodetic Reference Frame, North American Reference Frame, Sistema de Referencia

Geocéntrico para las Américas, Regional Reference Frame Sub-Commission for Europe, Asia–

Pacific Reference Frame (APREF) and Scientific Committee on Antarctic Research. A detailed

discussion of the historical role of the various reference frames is presented by Altamimi

&Gross (2017).

Terrestrial reference systems are realised by a combination of space-based measurement

methods, of which GNSS is a crucial component. All GNSS are constructed on top of an

independent reference system designed to match ITRF. For example, GPS uses the World

Geodetic System 1984 (WGS84), which is aligned with ITRF2000 (Hegarty, 2017).

GLONASS adopts the Parametry Zemli 1990 (PZ90) to match ITRF2000 (Revnivykh et al.,

2017). Galileo uses the Galileo Terrestrial Reference Frame (GTRF) defined by ITRF2005

within a few millimetres (Falcone et al., 2017). BeiDou uses the China Geodetic Coordinate

System 2000 (CGCS2000) referred to ITRF97 at the centimetre level (Yang et al., 2017). For

GNSS to be interoperable, transformation models are used to convert between reference frames

and time systems. This interoperability means that user equipment (UE) tracking measurements

from all GNSS can provide coordinates in any reference system.

Most countries have historically established local, non-geocentric reference systems that best

match and suit the needs of their local geography. Given the benefits in terms of increased

compatibility, standardisation and automation of adopting a global reference system, some

countries have been migrating to national datums that closely match ITRF and can be adjusted

over time using velocity vectors. Australia has moved from its Geocentric Datum of Australia

1994 (GDA94)—which coincided with ITRF92 at epoch 1994.0—to the Geocentric Datum of

Australia 2020 (GDA2020), which aligns with ITRF2014 epoch 2020.0, and will implement a

time-dependent Australian Terrestrial Reference Frame (ATRF). In the US, the current North

America Datum 1983 (NAD1983) will be replaced by the National Spatial Reference System

2022 (NSRS2022), which will have a similar time-dependent component.

At the local level in construction projects, agriculture or other applications defined by an area

of several hectares or less than the size of a municipal council, geodesy does not have the same

relevance as it does for large areas. For such applications, reference frames have often been

defined by an arbitrary coordinate system for plane mapping purposes, and the task of tying

into an official datum has been unnecessary for most projects. However, local reference frames

can be linked to GNSS or an external reference frame by process of measuring the local

reference stations with GNSS and ‘localising’ or ‘calibrating’ the GNSS to the local frame.

This process can be exported to various software applications in standard or custom file

formats.

With the advent of GNSS, local frames in professional sectors are increasingly disappearing in

favour of national reference frames and planar projection for mapping purposes. One reason

for this is that the potential large-scale adoption of GNSS and automation requires operation in

GNSS-compatible reference frames. It is expected that this trend will continue as such local

frames will be replaced by the national and thus global reference frame enabled by GNSS.

Complementary PNT methods that are non-GNSS operate on independent or arbitrary

reference frames. For example, a simultaneous localisation and mapping system can navigate

a robot in an arbitrary coordinate system independent of GNSS. This means that robotic tasks

are carried out relative to objects, not relative to an absolute coordinate system. Relative

positioning limits interoperability, compatibility, traceability and safety of the operation. It may

very well be technically possible to carry out operations in relative space. However, it will be

an easier positioning task if the objects and information can be related to absolute space.

The implementation of local reference frames from complementary PNT methods discussed in

Section 2.4 should be tied to GNSS and, by extension, global reference frames for wide-scale

adoption and interoperability. In the case of RF beacon techniques used for indoor navigation,

such as Bluetooth and Wi-Fi, the WGS84 coordinates for transmitting access points or beacon

infrastructure are necessary for ubiquitous positioning. This allows for a seamless transition

between outdoor positioning based on GNSS and indoor positioning based on indoor

positioning systems.

3.2 GEODETIC INFRASTRUCTURE: FROM GLOBAL TO LOCAL

Geodetic infrastructure refers to the network of monuments, observation instruments and

services that support geodetic measurement techniques (National Research Council, 2010).

Australia’s NPIC establishes a coordinated link between geodetic infrastructure and other

components that augment redundancy, integrity and continuity of GNSS positioning services

as discussed by Hausler (2014). Components like analysis centres, Analysis Centre Software

(ACS), and the network of commercial VARs are part of the NPIC. These provide services to

meet the new needs of, not only scientific applications (National Academies of Sciences,

Engineering, And Medicine, 2020) but also applications in industrial and public sectors. A

cross-sector link between physical infrastructure from a global to a local scale is essential to

deliver these new positioning services.

Physical infrastructure is made up of monuments spread throughout the continental mass that

have at some point in time been measured with different measurement techniques. These are

geodetic survey marks, and their coordinates are known in the reference frame. In contrast to

these marks, the points that are continuously measured as GNSS CORS for monitoring

purposes are known as active marks. These marks serve a dual purpose in that they can be used

to monitor a reference frame and provide services to the geospatial community for position

determination.

Geodetic infrastructure underpins the materialisation of a reference frame; that is, the

construction of physical points, commonly CORS, that have been measured to define the

mathematical parameters of the reference system. A hierarchical approach was proposed by

Rizos (2007) to organise CORS infrastructure in three levels of stations, defined from a global

to a local scale. The tiered approach is as follows:

• Tier 1: IGS stations augmented by high-quality stations from regional networks

• Tier 2: primary national geodetic networks

• Tier 3: state, secondary and private CORS networks.

The IGS maintains the highest-level network of geodetic infrastructure, which are used to

define the ITRF. The instruments and techniques used for this level of infrastructure often

combine redundant ground and celestial observation methods such as very-long-baseline

interferometry (VLBI); satellite laser ranging (SLR); Doppler Orbitography and

Radiopositioning Integrated by Satellite (DORIS); gravity measurements; and GNSS to tie in

the reference frame. Figure 3-1 shows an example of a typical Tier 1 site in Yarragadee,

Australia which includes the ‘YARR’ station—part of the IGS global network—and regional

networks such as the APREF, Australian Fiducial Network and Australian Regional GNSS

Network.

Figure 3-1 ITRF four-technique colocation site in Yarragadee, Western Australia (Hu, 2014)

Continuing down the hierarchy, regional level reference frames are defined by a denser

network of CORS to provide a link between the ITRF and national datums, providing

calculations of regional velocity fields and independent monitoring, among other benefits. For

example, the APREF network consists of over 420 stations from different countries and

institutions. Some sites are used in both the regional network and the ITRF network to provide

a connection between frames, but most sites are simpler installations of GNSS CORS. Figure

3-2 shows the monitoring station in Yulara (Northern Territory, Australia), which is part of the

APREF and AuScope (AuScope Limited, 2019) networks. The regional design of these sites

considers redundancy, stability and continuous operation in remote areas.

In every country, spatial information is referenced to an official national datum by a body of

legislation. For example, the official datum in Australia, GDA2020, was implemented by the

Australian Government on 11 October 2017 (Australian Government, 2017) following the

National Measurement Regulations 1999 and the National Measurement Act 1960 (Australian

Government, 2015). Under this legal framework, national and state-level CORS provide GNSS

users with a traceable connection to the country’s geodetic infrastructure.

Figure 3-2 APREF CORS Yulara, Northern Territory (YULA) (Hu, 2014)

The objective of this Tier 3 level of CORS is to meet users’ needs for GNSS PNT services.

Tier 3 level infrastructure does not have the strict requirements for redundancy, stability and

continuity of higher-level geodetic networks. As an example, for Tier 1 geodetic applications,

monuments installed on metal structures are unfit because they introduce multipath errors and

structural movement. For the application of NRTK, however, Tier 3 CORS structures (Figure

Figure 3-3 GPSNET CORS Lalber, Victoria (Hu, 2014)

3-3) are suitable and often used by state and commercial operators.

Different monumentation types have technical considerations that must be accounted for when

establishing CORS. UNAVCO has developed a classification for the level of monumentation

(Figure 3-4) following the tiered structure to suit a range of applications (UNAVCO, 2018).

UNAVCO’s document aims to summarise the options available for scientific GNSS users.

Currently, for most construction sites, farms and other local applications, CORS for RTK are

generally established as custom low-order monuments, in an ad-hoc manner with limited

Figure 3-4 Classification of GNSS monuments (UNAVCO, 2018)

consideration for tie-in to national datums, compatibility and scalability.

3.2.1 Improving Density with User-owned GNSS Infrastructure

Most CORS networks were initially used for scientific purposes. Given the growth of the

augmented GNSS market, commercial companies began to densify networks to provide better

services and coverage in niche markets. This densification race meant that different commercial

providers were deploying infrastructure in similar locations, thus duplicating coverage in a

similar way to how telecommunication infrastructure is deployed and competes on coverage.

Although government and commercial providers are densifying their Tier 3 networks,

professional users often opt for installing additional fit-for-purpose CORS to provide more

station density or localised coverage. One of the drivers for this is accuracy, represented as

baseline distance to RTK CORS. This lack of density was identified in the GSA’s 2017 Market

Report, which noted that current RTK network density (Figure 3-5) does not fully meet user

needs. Moreover, it is expected that CORS coverage and density will be extended to meet

demands for new industry segments (GSA, 2017).

There is a gap then between the density offered by some networks and the density required by

the most demanding applications. The GEONET network in Japan provides the highest density

among all national RTK networks, with interstation mean distances of 20 km (Figure 3-6). This

network allows for accuracies (67%) of 15 mm horizontal and 25 mm vertical (Tsuji et al.,

2018). When professional users in machine control and precision agriculture establish a local

CORS, they are looking to achieve optimal RTK performance in the order of less than 30-mm

accuracy with 95% confidence (see Appendix A for accuracy measure conversions). Dense

CORS at the level of more than 5 RTK stations/1000 km2 or 10-km station spacing is key to

Figure 3-5 Density of RTK reference stations (GSA, 2017)

Existing business models for CORS operators are adapting to user needs by densifying

providing the accuracy levels required by these industries (GSA, 2017).

networks, creating new opportunities for user-operated collaborations and allowing new

sectors to enter the GNSS services marketplace. To date, the deployment of CORS networks

has been the role of government geodetic agencies and geospatial service providers. However,

some essential factors in the development of dense CORS networks may come from the

growing interest by the telecommunication sector in high-accuracy positioning, as seen in

Japan by Softbank and NTT DOCOMO (North Coast Media LLC, 2019; NTT DOCOMO,

2019).

Telecommunication companies entering the market today will benefit from existing networks

and lower costs in GNSS hardware compared with the initial investment made by established

GNSS service providers while also taking advantage of the communication channel that they

own. If the mass-market creates a demand for high-accuracy positioning applications, it is an

opportunity for telecommunication companies to create their own denser CORS networks for

users in location-based services (LBS), autonomous vehicles and traditional positioning

industries. The professional market may also benefit from the introduction of new CORS

network providers in the telecommunication sector.

Figure 3-6 Relationship between network density and RTK accuracy (Tsuji et al., 2018)

3.2.2 The Importance of GNSS Infrastructure

From the system operator’s perspective, GNSS infrastructure is the complex network made up

of highly redundant and independent systems designed to maintain the continuity of operation

of GNSS space assets. The control segment is made up of control and processing stations,

ground monitoring stations and antenna stations for telemetry, tracking and command. The

antenna stations uplink navigation and integrity data to the satellites themselves—which make

up the space segment—and the correct operation of these entails that clocks, signals and orbits

are functioning in optimal conditions. Any changes to the operation of GNSS services are

notified to the public or user segment. The relationship between GNSS segments is shown in

Figure 3-7. The infrastructure maintained by all system operators can be considered the highest

level of infrastructure critical to the operation of GNSS. This is the level where the Australian

Government would operate the critical infrastructure necessary for an Australian SBAS.

However, there are essential networks of infrastructure at downstream levels.

For scientific users, GNSS infrastructure made up of the network of tracking stations and GNSS

CORS as measurement instruments, and it also entails the supporting infrastructure required to

generate products such as global reference frame solutions, precise orbits and clocks.

At a regional and national level, government organisations also operate networks of GNSS

CORS for adopting regional reference frames and establishing national datums. National

networks are closer to professional end-users so that they can generate additional products

directly accessed by the professional and mass-market segments. These include real-time

NTRIP RTK and DGNSS corrections, station observation files for manual post-processing and

on-demand PPP post-processing services. New products could include real-time PPP

Figure 3-7 GNSS segments (Sabatini et al., 2017)

corrections.

Commercial service providers also operate their own independent network of infrastructure;

for example, OmniSTAR operates a network of over 100 CORS spread across the globe to

provide its correction services. In addition, companies like Inmarsat which offer satellite

delivery for OmniSTAR operate a ground network of stations to maintain its communication

network. This level of private infrastructure and services depends directly on the operators and

scientific GNSS infrastructure. A failure in any part of this complex system will affect the user

segment regardless of the method they use to receive corrections.

If there were to be a critical failure or an attack on the systems that maintain the operation of

GPS, GLONASS, Galileo and BeiDou, several critical services would be affected, including

SoL, aviation, timing and communication. While this may seem unlikely to the general public,

it is one of the main reasons for the four independent GNSS in operation today. No nation

wants its critical services to be dependent on a system maintained by another nation and out of

their control. This is an aspect of national security that is evident in the military and government

sectors and where the public and professional sectors benefit from increased protection and

redundancy.

As the use of GNSS services spreads through national security sectors, the effect and

prominence of this infrastructure become more evident. This is part of the rationale used by the

US DHS in deeming GPS a ‘hidden utility’ for critical infrastructure sectors and why it is

working to improve resilience and security of GPS use (DHS Science and Technology

Directorate, 2016). In Australia, a similar approach is being prepared by the Australian

Government as part of the Australian Strategic Plan for GNSS (Australian Spatial Consortium,

2012), which proposed the NPIC as critical infrastructure for the development and national

security of the nation.

At the national level, infrastructure that sustains the national reference frame is of high

importance to the geodetic community and public users that indirectly access PNT services.

National-level CORS are also essential to the operation of professional applications and have

a significant economic impact on the industry (ACIL Allen Consulting, 2013a).

There is potential for complementary PNT infrastructure to increase its importance and be tied

to traditional positioning infrastructure, especially in the context of autonomous vehicles and

LBS. For this type of non-GNSS infrastructure, like RF beacons, deployment is currently done

for commercial purposes at a small scale. The impact of service interruption will be limited. If

a widely available positioning technique like WiFi, BT is adopted by automated systems, then

it should be tied into datum coordinates and connected as part of the GNSS infrastructure to

increase its benefit as well as protection.

3.3 PNT INFRASTRUCTURE IN CIVIL CONSTRUCTION

Civil construction projects are traditionally designed in a local plane with arbitrary coordinates

and independent horizontal and vertical datums. It is the task of engineering and construction

surveyors to define these local datums and monitor works for civil construction sites by

establishing networks of reference marks or control points. The nature of these control

networks is changing because of the increasing reliance on GNSS in construction operations.

The traditional measurement instruments used by construction surveyors, such as total stations,

spirit and laser levels, rely on direct line-of-sight (LOS) observations between reference marks

and construction elements. In contrast, GNSS measurements are not LOS dependent, so the

amount of physical marks supporting construction tasks is reduced and replaced by a local

CORS.

Installation of permanent CORS in construction sites presents several challenges; for example,

power, security and permanent structures are not always available. Thus, temporary RTK base

stations are set up using custom monumentation (Figure 3-8). These monuments are meant to

be used for the duration of a project, often several months or years, and can be dismantled

easily and relocated when required. A more robust solution is to establish CORS in suitable

structures for Tier 3 stations like residential or commercial buildings owned or related to the

construction firm. This is often done in collaboration with a NRTK service provider, which can

incorporate the station into a more extensive CORS network in return for a financial or service

incentive. When there are mobile Internet blackspots on a job site, bases are equipped with

Figure 3-8 Construction site GNSS base station

Performance in terms of accuracy and continuity is one of the main considerations for civil

additional UHF radios to guarantee communication.

construction users to increase adoption of NRTK services and contribute to the rollout of CORS

infrastructure. Construction sites have been early adopters of GNSS and traditionally used

UHF-delivered RTK onsite before the wide-scale availability of mobile Internet. UHF

transmission protocols have been standardised so that multiple equipment brands could connect

to the same base station. This compatibility was necessary given the range of independent firms

working on a construction site. The distance range of UHF radio communication also meant

that RTK could only work within 5 km in most cases. Now, civil construction users expect the

performance and continuity of short-range RTK. The degradation in accuracy is noticeable,

especially in the vertical component, when using NRTK with long baselines.

Some construction firms see advantages in moving from several disconnected and temporary

UHF base stations on each project to a unified CORS network. By partnering with a NRTK

provider, they can contribute access to new CORS sites in return for discounted subscriptions

and freeing themselves from the operation and maintenance of equipment. There are, however,

larger firms with too many users to make this collaboration model feasible. In this case,

construction firms deploy and operate their own independent CORS network with coverage in

their specific sites. This change in business models can be expected to continue as GNSS

becomes more widely used in the industry.

3.4 PNT INFRASTRUCTURE IN AGRICULTURE

Unlike on construction sites, where the environment is continually changing, and structures are

being built, the environment around farms is relatively constant. There is little need for survey

marks. The primary and often only part of PNT infrastructure is a CORS used as a RTK base

station for precision farming.

Farming operations are increasingly relying on GNSS for positioning and navigation of

machinery and robots. The accuracy required to perform some tasks on farms is at the level of

several metres, in which case standalone GNSS without any additional local infrastructure and

augmentation is used. For sub-metre and decimetre work, equipment manufacturers such as

John Deere have begun offering increased accuracy services, like PPP, over satellite L-band.

For precision farming requiring centimetre accuracy, farmers would set up local RTK base

stations.

The quality of the CORS in agriculture is of a similar level to the custom or Tier 3 monuments

discussed for civil construction in Section 3.3. Given that agriculture users are in rural areas

with limited mobile Internet coverage, farms often need to deliver corrections through UHF or

WiFi mesh networks. If Internet coverage is guaranteed, partnerships between farmers and

equipment dealers are arranged to form wide-area NRTK CORS. The benefit of tying together

CORS is that they operate in the same datum and users can continue to work from several bases

in the case of an outage on a local base station. There is a range of commercial networks

directed at the agriculture market that exist in parallel and often overlap other government or

commercial networks in different industries.

The RTK method is so widespread in agriculture that farming cooperatives have joined their

independently owned base stations into small networks of single-base RTK delivered over the

Internet. Some farming companies have improved these networks by aggregating bases from

single farmers, cooperatives and dealers to deliver NRTK services over more extensive areas

for subscription fees. Some of these highly shared and low-cost business models are unique to

farming, where cooperation has expanded the use of GNSS. One example of this is RTK CLUE

(Reichhardt GmbH Steuerungstechnik, 2018), which since 2011 has allowed users to access

third-party bases and upload their base correction data to the shared network.

3.5 PNT INFRASTRUCTURE IN RAIL TRANSPORT

Rail management systems are made up of complex infrastructure and have long historical

dependencies that predate GNSS. This infrastructure includes the communication, safety and

tracking systems of rail networks and can provide positioning and control without GNSS.

Modern automatic train control systems (ATCS) are being upgraded to include GNSS

positioning, but its adoption has been slow, and several safety requirements must be considered

in its implementation. The European Railway Traffic Management System (ERTMS) in

Europe is an example of a system that is currently being reviewed to include GNSS (Sabina et

al., 2018).

The ERTMS uses SBAS for metre-level tracking and positioning of trains. Several other rail

management systems use satellite communication to guarantee data link coverage over a vast

area of operation. Additionally, few applications in rail require centimetre-level positioning, so

dedicated CORS infrastructure, as is seen for civil construction, is uncommon in the rail sector.

There are, however, some PNT applications separate from train control or safety that require

higher accuracy. For example, structural monitoring on rail tracks, civil and possession works

would use RTK or post-processed GNSS from nearby CORS. If higher-accuracy applications

are adopted in rail transport, as is proposed for ATCS on high-density lines, new forms of

positioning infrastructure will likely be deployed.

3.6 SUMMARY

This chapter reviewed the user expectations of a modern GNSS positioning service in the

scientific, public and professional domains, as outlined in research question 2. It has also

discussed the importance of GNSS infrastructure for the maintenance of GNSS services and

how it serves to interconnect reference frames from a global outlook to a local perspective.

The impact of GNSS on socio-economic activities is significant. Therefore, the highest level

of GNSS infrastructure is considered critical infrastructure by the governments of several

countries. As demand for high-accuracy positioning increases, so does the importance of GNSS

augmentation services and additional CORS infrastructure.

There are similarities between the role and future rollout of GNSS infrastructure and how the

telecommunication industry has expanded its coverage and services. This is evident in the

participation of Japanese telecommunication companies in the deployment of CORS

infrastructure. Other regions, such as China, US and Europe are following these developments

with similar partnerships. GNSS and connectivity can now be considered fundamental

components of how society and industries operate.

The CORS density of national or telecommunication networks may not be sufficient for the

most demanding professional high-accuracy applications. The densification of CORS in

Australia and other countries has contributed to the growth of GNSS positioning services like

NRTK. Despite this, there are still gaps in coverage for professional high-accuracy users.

Civil constructions sites, mines, farms and rail corridors still require temporary and dedicated-

RTK infrastructure. This infrastructure provides higher accuracy by maintaining very short

baseline distances. Also, it provides the guarantee of a dedicated communication channel when

mobile Internet is not available. Further, it is the most cost-efficient solution for several

applications. The GNSS services that make use of GNSS infrastructure and serve the

positioning requirements of professional users will be discussed in the next chapter.

CHAPTER 4: GNSS AUGMENTATION

SERVICES FOR PROFESSIONAL HIGH-

ACCURACY APPLICATIONS

4 GNSS AUGMENTATION SERVICES FOR

PROFESSIONAL HIGH-ACCURACY APPLICATIONS

The existing commercial and public PNT services market delivers fit-for-purpose products to

a range of industry sectors and applications. Most products are in the form of GNSS

augmentation services for professional users that rely on the techniques and infrastructure

outlined in Chapters 2 and 3, respectively. As the NPIC takes form in Australia, it is expected

that the existing services market will be affected. The public availability of nationwide dense

CORS infrastructure promises to reduce barriers to adoption for new service providers in the

marketplace.

This review aims to describe the PNT services that are currently available to users from

commercial and public providers in Australia. The data was collected from product brochures,

company websites, whitepapers, experimental results and presented in a uniform format.

Additionally, this section discusses the existing marketplace trends and opportunities that may

affect users in the future.

4.1 GNSS AUGMENTATION SERVICES MARKETPLACE

Commercial companies have offered satellite-based correction services since the late 1990s

with subscription-based, proprietary correction signals transmitted over satellite L-band to

compatible receiver hardware. Hardware compatibility at the user end allows companies to

control access and licensing to authorised users. These services met market demand for high-

accuracy positioning in areas where satellite communication was the only available delivery

channel. The first of these companies was OmniSTAR, which focused on the offshore

positioning market. At the same time, John Deere introduced a similar service, named StarFire,

aimed at the agriculture market. Since then, several commercial offerings in the geospatial

space have populated the market with GNSS augmentation services.

Several research organisations currently generate PPP correction services available free of

charge via the Internet. These are different from the ones used by commercial providers in that

the latter offer additional levels of service, support, quality and a delivery channel via satellite

L-band.

In the public realm, SBAS have covered regions of the world, delivering integrity parameters

and metre-level positioning solutions to support the aviation sector. Australia and New Zealand

have begun testing an SBAS for the Oceania region that includes both aviation-style SBAS

metre-level corrections and PPP decimetre-level corrections. This approach of offering two

levels of public access augmentation services is currently implemented by QZSS and proposed

by BeiDou. In contrast, Galileo and GLONASS use independent systems for their SBAS

service, namely EGNOS and SDCM.

Table 4-1 to Table 4-5, presented at the end of the chapter, summarise currently available

GNSS augmentation providers and services in Australia and their characteristics. Accuracy

measures are expressed at the 95% confidence interval. However, some manufacturers use

different criteria, and their specifications can be standardised using the accuracy measure tables

in Appendix A. While this list is not exhaustive, it covers most providers that could be

identified by the author. Some PPP providers have been omitted as they focus exclusively on

the marine and offshore markets, and the objective of this list is to provide solutions to

applications on land in the context of civil construction, agriculture and rail transport. Some

services aimed at the autonomous vehicle sector are, however, included as they can potentially

be used in these industries.

The types of provider can be described by sector as follows:

• Navigation satellite system provider: GNSS or RNSS providers that deliver

augmentation services for the public, as done by the Japanese CLAS service, without

the need for additional hardware or subscription.

• Government: Australian Government institutions that provide open-access services to

the public, such as Auscors, or subscription services, as previously done by Vicpos.

• Research: Institutions that provide services to the international research community,

such as the real-time IGS network.

• Commercial: Privately owned companies that deliver augmentation services for paying

customers through subscription services, such as OmniSTAR.

• User-owned: Individual users that operate their own augmentation infrastructure and

services to meet specific industry uses.

The coverage areas can be defined as the regions where the solution is applicable within a

specified level of performance and where delivery via a communication channel can be

established:

• Global: augmentation services with global applicability, such as the commercial

OmniSTAR and the open-access Galileo HAS, delivered via a constellation of satellites

with a global footprint.

• Regional: augmentation services with either regional applicability, such as QZSS

CLAS and Vicpos, or regional communication coverage because of limitations in the

delivery method, such as mobile Internet.

• Local: augmentation services that serve a dedicated temporal or spatial coverage for

specific industry use, such as a local mine site or rail network.

The delivery method or communication channel can be classified as:

• L-band (navigation satellite system provider): system providers include augmentation

services, such as QZSS CLAS and Galileo HAS, in their public access signals without

the need for additional hardware or subscriptions.

• L-band (Commercial): privately-owned companies deliver augmentation services for

paying customers through L-band signals from commercial communication satellites,

such as Inmarsat.

• Internet: commercial or open-access augmentation service available over the mobile

Internet, WiFi mesh or any other delivery method.

• Radio: most user-owned augmentation methods using RTK, have local coverage and

are transmitted over radio band (UHF, 900 MHz, 2400 MHz) for specific industry uses.

The summary of augmentation services also shows the supported GNSS constellations, the

solution type (DGNSS, WADGNSS, PPP, PPP-AR, SSR-RTK, NRTK, RTK) and accuracies.

Accuracy is specified at the 95% confidence level unless otherwise noted to standardise

nomenclature. These figures have been gathered from service providers’ specifications or

previous studies demonstrating experimental results. DGNSS and RTK accuracies do not

consider baseline distance effects.

4.2 PPP AUGMENTATION SERVICES PROVIDED BY GNSS AND RNSS

OPERATORS

Several GNSS public augmentation services have global and regional coverage designed to

meet specific industry requirements. For example, since the late 1990s, the various

implementations of regional SBAS have been a type of public, regional, WADGNSS

augmentation delivered via L-band for the aviation sector. These SBAS services have provided

metre-level accuracies with increased integrity messages for SoL applications to the public,

while commercial providers focused on developing services with higher accuracy using PPP

for niche markets.

With the development of next-generation navigation systems like QZSS and Galileo, there was

an opportunity for system providers to deliver high-accuracy PPP services to the public, similar

to commercial products. In 2018, the QZSS CLAS service launched as a public, regional SSR-

RTK augmentation for the Japanese region delivered via L-band for various industries. This

service is aimed at surveying, construction and agriculture industries, which have been

traditionally the domain of commercial providers. At the time of writing, the Galileo

programme had also announced HAS for 2020 as a public, augmentation service with global

coverage to support diverse applications (Fernández-Hernández et al., 2018).

The justification for developing these new public services with increased accuracy and integrity

has been to advance the adoption of GNSS technologies across different industries. The

proposed services are currently focused on the system operators’ regions (Europe and Japan).

However, there is a commitment to deliver these services globally for Galileo HAS on the E6

frequency (Fernández-Hernández et al., 2018) and to expand the regional coverage to include

all of Asia–Pacific for the QZSS MADOCA service on the L6E frequency (Harima et al.,

2017). There is also the potential for the BeiDou regional PPP service transmitted on the B2b

signal of the GEO satellites to be expanded to cover a broader region.

The Australian Government launched its Australian SBAS Testbed in 2017 with dual-

frequency SBAS and PPP capability delivered over L1 and L5 on a commercial Inmarsat

satellite. The project has been funded until 2022 and aims to have an operational system for

public access with specifics under investigation. Given the availability of public access

solutions, it is expected that a range of services will be available in Australia by 2022. Within

this context, the current and proposed PPP augmentation services provided by GNSS and

RNSS operators with potential coverage in Australia are shown in Table 4-1.

4.3 RESEARCH AND GOVERNMENT PPP AUGMENTATION SERVICES IN

AUSTRALIA

Since 2001, PPP has been possible thanks to the calculation of precise orbits and clocks by the

IGS and associated research organisations. While these products are mainly for research use,

there have been downstream uses developed in the commercial sector. For example, the Jet

Propulsion Laboratory (JPL) is a contributing analysis centre for the IGS and has historically

computed orbits and clock estimates for the scientific community while also providing real-

time products commercially to companies such as OmniSTAR, which deliver a PPP service to

end-users in niche markets like offshore maritime and agriculture.

In a different approach, some privately held companies, like GMV, can also contribute to the

IGS by calculating independent orbits and clock products; this is the case for the magicPPP

real-time service from GMV (Samper et al., 2014), available for both research and commercial

use. This company was contracted by the Australian Government to develop a PPP

demonstration for the Australian SBAS Testbed project and is involved with the ground control

systems of Galileo (GMV, 2018).

In 2013, the IGS began streaming real-time products for PPP directly through their open access

NTRIP caster for research purposes (Elsobeiey & Al-Harbi, 2015; International GNSS Service,

2016). Shortly after, the Centre National d’Études Spatiales (CNES) PPP Wizard project

(Laurichesse & Privat, 2015) also generated open access PPP products for research purposes.

Today there are additional real-time streams from a range of analysis centres including the

Bundesamt für Kartographie und Geodäsie (BKG), Deutsches Zentrum für Luft- und

Raumfahrt (DLR), European Space Agency (ESA), Deutsches GeoForschungsZentrum (GFZ)

and Chinese Academy of Sciences (CAS) with varying degrees of accuracy (Wang et al., 2018).

These products are openly available and often used by small hardware integrators and end-

users to generate PPP solutions in emerging markets.

It is possible for established commercial companies also to utilise these real-time products to

generate PPP augmentation services and to implement them in GNSS receiver hardware. For

example, the chipset manufacturer, u-blox, allows integrators the ability to input PPP and RTK

corrections from any service provider for carrier phase augmentation in some of their chips.

Even though this can offer consumers a low-cost solution, some manufacturers opt for full

control in offering end-users an integrated positioning solution or delivering business-to-

business (B2B) services. This is discussed further in Section 4.4.

In conjunction with the Australian SBAS, the NPIC project promises to generate correction

streams through their ACS. This service offered by the government can complement other

research and commercial products in the market to promote wide-scale adoption of high-

accuracy GNSS. The ACS products will be utilised by private companies that can value-add

along the supply chain, or directly by end-users. The public research and government PPP

products available are shown in Table 4-2.

4.4 COMMERCIAL PPP AUGMENTATION SERVICES IN AUSTRALIA

In parallel with public offerings, there are commercial WADGNSS and PPP services with

global coverage, such as OmniSTAR, StarFire, that mainly support offshore maritime

applications and other industries. Similarly, regional NRTK services have been established all

over the world as commercial or government products for high-accuracy land applications such

as surveying and agriculture.

The first L-band subscription service, now operating as Trimble OmniSTAR, provides

correction signals including single-frequency (L1) WADGNSS and dual-frequency (L1+L2)

PPP (Trimble Navigation Limited, 2016). The same company launched Trimble RTX to

additionally provide PPP-AR and SSR-RTK services using dense regional networks of their

own reference stations, and JPL commercial orbit and clock products (Chen et al., 2015; Doucet

et al., 2012; Trimble Navigation Limited, 2016). Both of these services are available in

Australia and worldwide, with some products allowing rapid convergence in selected regions.

Hexagon AB is the parent company of Novatel, Terrastar and Veripos. Novatel has delivered

WADGNSS and PPP services since 2001 (Jokinen et al., 2014). Terrastar operates three levels

of services for land applications (Terrastar, 2016), while Veripos provides six additional PPP-

AR and SSR-RTK products for marine applications (VERIPOS, 2017). NavCom, owned by

John Deere, distributes the StarFire service, which is mainly focused on agricultural

applications (NavCom Technology, 2016). Hemisphere launched their Atlas service in 2016,

with three levels of accuracy—H100, H30 and H10 (Hemisphere GNSS, 2016)—for a range

of applications including agriculture and construction. For a full list of existing commercial

PPP services in Australia, see Table 4-3.

Finding the balance between the government-funded, publicly available correction services

mentioned in Section 4.2 and commercial offerings has been a topic of discussion since private

companies began consolidating and system operators announced plans to offer their own PPP

services (North Coast Media LLC, 2014). One of the most commonly cited points of difference

for these commercial offerings is in the level of service and the demands of markets for which

they are providing solutions. Commercial services began offering solutions to the offshore

marine market where accurate positioning was unattainable because of long distances from

CORS and lack of communication coverage from SBAS. As these services matured, companies

offered service and customer support tailored to the high demand of these markets. In offshore

marine construction, downtime caused by a service outage can mean immense operational costs

and loss of profit, so commercial providers should provide guaranteed uptime and service-level

agreements with end-users. Currently, there are no service-level agreements or explicit

guarantees of performance. However, it is unlikely that most professional end-users in markets

like marine, construction, mining, agriculture and rail would adopt a service from a public or

research provider to reduce costs while compromising on the current level of service.

For markets such as the automotive industry with complex systems and regulations,

manufacturers are creating partnerships with commercial PPP service providers to deliver an

integrated solution for high-accuracy positioning. For example, Sapcorda Services is a joint

venture of u-blox, Mitsubishi Electric, Bosch and Geo++ (a PPP service provider), whose goal

is to deliver an integrated high-accuracy solution to the automotive and mass-markets (u-blox,

2017). Similarly, Swift Navigation has several hardware products and a correction service,

named Skylark, aimed at the automotive and robotics markets with coverage in selected regions

of the US (Swift Navigation, 2018). A similar approach is being used in China by Qianxun SI

(Qianxun Spatial Intelligence Inc., 2019), which has also deployed a network of 2,200 CORS.

As these services are marketed to autonomous cars where SoL is commonly required, the

service should be controlled in a way that performance can be guaranteed so that integrity is

an essential part of the service offering. These emerging PPP service providers are listed in

Table 4-4.

There is growing interest in developing complementary satellite constellations in LEO as

augmentation systems. Some proposals have been developed by system operators themselves,

including BeiDou (Li et al., 2019; Meng et al., 2018). Other proposals by private satellite

communication companies including OneWeb, SpaceX, Boeing and Iridium promise to cover

the Earth with 5G communication links and deliver PPP augmentation on a dedicated satellite

Internet link (Reid et al., 2018). The proposal by Iridium is a particular case in that the network

of LEO communication satellites can be used to both deliver corrections and perform

independent satellite positioning via a service called Satellite Time and Location (STL) (North

Coast Media LLC, 2017b). These providers may offer commercial augmentation services in

the future. Technical details around compatibility, performance and characteristics of receiving

terminals are not yet available; thus, they are not considered in the scope of this research.

4.5 RTK AND DGNSS SERVICES IN AUSTRALIA

The highest-order GNSS CORS infrastructure in Australia is owned and operated by the federal

government as part of Geoscience Australia’s Auscors network where RTK services can be

accessed for free by the public. The Victorian and New South Wales governments have

operated denser networks with access fees for industry use in their states (Millner et al., 2006;

White et al., 2009) but the business model is migrating to free access as Auscors absorb the

networks under the NPIC.

Commercial GNSS manufacturers, service providers and VARs use government networks with

additional infrastructure in-fill to provide NRTK and DGNSS subscription services. NRTK

have traditionally met the requirements of high-accuracy applications where mobile Internet

coverage is available. The current NRTK services in Australia are summarised in Table 4-5.

As part of the new NPIC proposal, there will be public access to all RTK streams from Auscors

sites and state government CORS, which since 2019 have charged access fees. This open data

policy established by the federal government will allow commercial providers to access CORS

data for free and continue to provide value-added services in the form of higher-density NRTK,

PPP-AR and SSR-RTK products. This will also allow newer companies and end-users to

directly access RTK data, thus increasing competition and reducing barriers to entry and cost

to end-users.

Outside Australia, several companies are creating new community-oriented business models;

one example of this is RTK CLUE in Europe (Reichhardt GmbH Steuerungstechnik, 2018).

Other services such as HIVE in Russia are deploying an extensive network of low-cost CORS

infrastructure and aggregating CORS from different sources into a single service that offers

ease of use and convenience (Industrial Geodetic Systems R&D, 2018). A similar approach is

being proposed by the Planet Accuracy Simpler network in China. This service adds a token

economy model based on blockchain transactions to incentivise users to upload data (PASNET,

2019). These international companies, together with others like Swift Navigation and Qianxun

SI, can enter the Australian market to deliver VAR services and hardware products.

Even with the availability of different levels of commercial and public corrections services,

some users decide to transmit their own RTK corrections via radio or Internet. The two factors

maintaining user-owned RTK bases as a preferred method for some applications are

performance and cost.

In terms of performance, some applications require repeatable accuracy in the order of 3 cm,

which cannot currently be achieved with PPP but is a typical use case for short-baseline RTK.

Most surveying companies own a UHF RTK base and rover, which ensures optimal

performance because of the short RTK baseline. By using UHF, the user also guarantees the

equipment will work in remote areas with no mobile Internet coverage. Another important

limiting factor of PPP is its long convergence time, where users can currently wait between 1

minute and 1 hour for accurate positions to be available. In contrast, RTK fixes to centimetre

level within seconds. User-owned RTK bases or NRTK services provided by commercial

VARs are often the only solutions for applications that require performance in the centimetre-

level.

Apart from technical factors, there is a significant cost incentive to operate an RTK base with

corrections over UHF. In this scenario, users have the operating costs of the RTK base

hardware and an annual ACMA fee, which in some cases is a better financial decision than

taking out individual PPP or NRTK subscriptions for a fleet of machines on a civil construction

site or farm.

Some commercial services have adapted their services to include NTRIP server subscriptions

for short-baseline RTK. Examples of such as the Trimble Internet Base Station Service (IBSS)

(Trimble Inc., 2018), Topcon MAGNET Relay (Topcon Positioning Systems, 2018) and

RTK2GO (SubCarrier Systems Corp, 2018). These services allow a user to access their own

RTK base corrections over the Internet for a smaller fee than a traditional subscription. The

current user-owned RTK services in Australia are summarised in Table 4-6.

The approach of user-owned RTK is a cost-effective solution for users that operate large

numbers of machines over a small area where the cost model of other subscription services is

not scalable. If the uptake of high-accuracy positioning for a mass-market such as LBS and

transport is to be promoted, it is necessary then to lower the price of augmentation services or

change to a public access model for these markets.

4.6 SUMMARY

This chapter has provided a survey of market data tabulated from existing and proposed GNSS

augmentation services in the Australian market. Commercial providers offer PPP and RTK

products under different levels of service, from metre to centimetre level accuracy. Some

commercial providers are consolidated under a parent company and deliver services to different

markets. There are also research and open-access government initiatives that can and have been

used to develop positioning products. These present opportunities for new companies to deliver

low-cost PPP services over the Internet. One of the most recent developments, however, is the

delivery of PPP services from GNSS system providers.

GNSS system providers are currently offering or plan to offer high-accuracy services based on

PPP with regional and global coverage. QZSS and Galileo are the first movers in this space,

but BeiDou and GLONASS have the technical capability to offer similar services, and there is

the potential for these services to extend their coverage into Australia. These services are

additional offerings to the Australian SBAS project, which will fill the gap of providing public

access DFMC SBAS and PPP.

The marketplace for PPP and NRTK service providers will potentially see more competition

and reduction in costs because of the availability of public services. It is expected that the free

high-accuracy services will lead to increased uptake for new applications. Existing commercial

providers will not be disrupted in their niche markets of high-accuracy and high-integrity

services. The main application of L-band delivered services is for high-integrity, unconnected

and remote areas, but with new markets that rely on connectivity (e.g. autonomous vehicles,

robotics) the option for Internet delivery is becoming more popular.

High-accuracy applications still require RTK to achieve centimetre-level positioning. Several

services offer access to NTRIP servers so that users can transmit corrections from their own

RTK bases. It is expected that this user-owned RTK approach will continue to grow in

popularity filling a market gap for cost-sensitive applications.

There is growing interest in developing PPP research products into commercial solutions for

end-users. The NPIC will generate additional services that can be publicly accessed by

hardware integrators to advance the adoption of GNSS technologies.

Table 4-1 Summary of PPP augmentation services provided by GNSS/RNSS operators

Accuracy (95%)

Provider

Service

Area

Augmentation signal GNSS1

Solution

Horizontal Vertical

CLAS

Regional (Japan)

L6 band (L6D)

G+R+E+J SSR-RTK <12 cm

<24 cm

QZSS

MADOCA Regional (APAC3)

L6 band (L6E)

G+R+E+J PPP-AR

Galileo

HAS

Global

E6 band (E6B)

<20 cm

GLONASS / SDCM SDCM 2

PPP

BeiDou

Regional (APAC)

B2b band

Regional (Australia)

PPP

10 cm

Australian SBAS G+E 1 GNSS constellations: GPS (G), GLONASS €, Galileo (E), BeiDou (B), QZSS (J) 2 Proposed 3 Asia Pacific Region

Table 4-2 Summary of research and government PPP augmentation services delivered over Internet

Provider

Service

Type

Area

GNSS1

Solution

IGS

IGS03

Research

Global

G+R

PPP

CLK91

CNES

Research

Global

G+R+E+B

PPP-AR

CLK93

BKG

CLK11

Research

Global

G+R

PPP

CLK20

G+R+E+B

PPP

DLR

Research

Global

CLK22

PPP-AR

ESA

CLK51

PPP

Global

Research

GFZ

CLK70

G+R+E+B

PPP

Global

Research

CAS

CAS01

G+R+E+B

PPP-AR

Global

Research

MagicPPP

Commercial

GMV

Global

G+R+E+B

PPP-AR

CLK80

Research

Geoscience Australia ACS

Government Regional

G+R+B+J

PPP-AR

1 GNSS constellations: GPS (G), GLONASS (R), Galileo (E), BeiDou (B), QZSS (J)

Table 4-3 Summary of existing commercial PPP augmentation services available in Australia

Accuracy (95%)

Provider

Service

Area

Delivery2

GNSS1

Solution

Notes

Convergence time

Horizontal Vertical

Global

Trimble/ OminSTAR

WADGNSS 1 m PPP-AR PPP-AR PPP-AR

8 -10 cm 8 -10 cm 8 -10 cm

Trimble

VBS XP G2 HP Viewpoint RTX Rangepoint RTX Fieldpoint RTX

L-band L-band L-band L-band L-band, (I) L-band, (I) L-band, (I)

G G G+R G G+R+E+B+J WADGNSS 1 m G+R+E+B+J PPP-AR G+R+E+B+J SSR-RTK

50 cm 20 cm

NA NA NA NA NA NA NA

Global/ Regional

Centerpoint RTX

Regional SSR-RTK coverage

L-band, (I)

G+R+E+B+J SSR-RTK

2.5 cm

5 cm (68 %)

<1 min <45 min <20 min <45 min <5 min <5 min <1 min / 15 min Converge time Fast/Standard <2 min / <20 min

NavCom

Global

5 cm (68%)

StarFire TerraStar-L

L-band, (I) L-band, (I)

G+R G+R

PPP-AR WADGNSS 50 cm

TerraStar-C

L-band, (I)

G+R

PPP-AR

5 cm

30 – 45 min

Hexagon AB/ Novatel

Global/ Regional

Regional SSR-RTK coverage

Apex is based on Veripos OCDS3 network

Hexagon AB

Global

Ultra is based on JPL OCDS3 network

3 cm 2.5 cm 5 cm 5 cm 5 cm 10 cm 10 cm

Standard is single-frequency

Hemisphere

Global

L-band, (I) L-band, (I) L-band, (I) L-band, (I) L-band, (I) L-band, (I) L-band, (I) L-band, (I) L-band, (I) L-band, (I) L-band, (I) L-band, (I)

G+R

Septentrio

Global

L-band, (I)

TerraStar-C PRO TerraStar-X Veripos Apex Veripos Apex2 Veripos Apex5 Veripos Ultra Veripos Ultra2 Veripos Standard Veripos Standard2 Atlas Basic Atlas H30 Atlas H10 SECORX-C SECORX-D TopNET Global

PPP-AR G+R+E+B SSR-RTK G+R+E+B PPP-AR G G+R PPP-AR G+R+E+B+J PPP-AR PPP-AR G PPP-AR G+R WADGNSS 1 m G WADGNSS 1 m G+R WADGNSS 50 cm G+R+B 30 cm PPP-AR G+R+B 8 cm PPP-AR G+R+B PPP-AR 4 cm (68%) PPP-AR 6 cm (68%) <20 cm PPP-AR

G+R+B+J

10 cm (68%) 30 – 45 min 60 cm (68%) <5 min 6.5 cm (68%) 5 cm (68%) 5 cm (68%) 12 cm 12 cm 12 cm 20 cm 20 cm NA NA NA NA NA 6 cm (68%) 9 cm (68%) NA

<18 min <1 min NA NA NA NA NA NA NA <5 min 10 - 40 min 10 - 40 min NA NA NA

Based on TerraStar services Based on TerraStar services

Topcon

Global

L-band

1 GNSS constellations: GPS (G), GLONASS (R), Galileo (E), BeiDou (B), QZSS (J) 2 Delivery: L-band, Internet (I) 3 Orbit and Clock Determination System

Table 4-4 Summary of emerging commercial PPP augmentation services with potential coverage in Australia

Accuracy (95%)

Provider

Service

Area

Delivery2

GNSS1

Solution

Notes

Convergence time

Horizontal Vertical

Swift Navigation GÉOFLEX

Regional Global

Internet Internet

G+R+B+J G+R+E+B

PPP PPP-AR

NA 4 cm

NA >30 min

NA NA

CNES PPP Wizard

Spaceopal

Global

Internet

G+E

PPP

DLR Reticle

EXO Technologies

Skylark GÉOFLEX NAVCAST (beta) PICO

Global

Internet

SAPCORDA

SAPA

Global

Internet

Qianxun SI

Global

L-band, (I)

G+R+E+B

Universe Voice Plan

PPP PPP SSR-RTK PPP SSR-RTK

GPAS

MADOCA

Regional

L-band, (I)

G+R+E+J

PPP-AR

Commercial or public model not yet confirmed

1 GNSS constellations: GPS (G), GLONASS (R), Galileo (E), BeiDou (B), QZSS (J) 2 Delivery: L-band, Internet (I)

Table 4-5 Summary of RTK and DGNSS positioning services with Internet delivery and regional coverage in Australia

Provider

Service

Type

GNSS1

Solution

Geoscience Australia

AUSCORS

Government G+R+E+B+J RTK

Trimble

VRS Now

Commercial G+R+E+B+J

RTKnetwest

Commercial G+R

Leica Geosystems

Smartnet Aus

Commercial G+R+E+B+J

SST GPS

Commercial G+R

Position Partners

AllDayRTK

Commercial G+R+E+B+J

Accuracy (95%) Horizontal Vertical <3 cm <1 m <3 cm <1 m <3 cm <1 m <3 cm <1 m <3 cm <1 m <3 cm

<5 cm <3 m <5 cm <3 m <5 cm <3 m <5 cm <3 m <5 cm <3 m <5 cm

DGNSS NRTK DGNSS NRTK DGNSS NRTK DGNSS NRTK DGNSS NRTK

1 GNSS constellations: GPS (G), GLONASS (R), Galileo (E), BeiDou (B), QZSS (J)

Table 4-6 Summary of user-owned RTK positioning services with local coverage in Australia

Provider

Service

Type

Delivery

GNSS1

Solution

Accuracy (95%) Horizontal Vertical

User-owned RTK

User owned

G+R+E+B+J RTK

<3 cm

<5 cm

Trimble Topcon Position Partners

IBSS MAGNET Relay MiRTK

Radio, Internet Internet Internet Internet

G+R+E+B+J RTK G+R+E+B+J RTK G+R+E+B+J RTK

<3 cm <3 cm <3 cm

<5 cm <5 cm <5 cm

SubCarrier Systems Corp

RTK2GO

Internet

G+R+E+B+J RTK

<3 cm

<5 cm

PASNET

Internet

G+R+E+B+J RTK

<3 cm

<5 cm

Commercial Commercial Commercial Open Commercial Open Commercial

1 GNSS constellations: GPS (G), GLONASS (R), Galileo (E), BeiDou (B), QZSS (J) 2 Supported constellations dependent on UE

CHAPTER 5: PNT REQUIREMENTS FOR

PROFESSIONAL HIGH-ACCURACY

APPLICATIONS

5 PNT REQUIREMENTS FOR PROFESSIONAL HIGH-

ACCURACY APPLICATIONS

GNSS providers deliver PNT services to a broad range of public users. Each user segment has

different requirements that are considered by GNSS system providers. Some industry bodies

regulate and promote the use of GNSS and PNT products in general, based on user requirement

frameworks. There are varying maturity levels of requirement frameworks in different sectors;

for example, aviation has well-defined PNT requirement parameters while civil construction

does not have standard documents defining user requirements. As GNSS become ubiquitous in

society, it is the role of each industry to define a PNT requirements framework for its users.

Specific requirements vary by industry sector and use case. Some, like aviation, emphasise

high integrity and availability; others, like surveying, primarily expect high accuracy. In

addition, PNT technologies are being used in emerging sectors with complex and undefined

requirements. For example, connectivity and interoperability are essential requirements for

relative positioning in the C-ITS and Internet of Things (IoT) sectors. The GSA, in its GNSS

User Technology Report (GSA, 2016 p. 72), identified this concern:

‘As GNSS-enabled applications become more demanding and move towards full

automation, not only of specialised robots in a controlled environment but also of

individual vehicles driving on the roads, more complex safety-related problems arise.

For example, an autonomous vehicle must not only determine its absolute position

with very high-accuracy and integrity, it is also compulsory to know this position

with respect to the always changing environment, composed among other ‘things’ of

other vehicles and of human beings.’

Several sectors, including automotive and telecommunications, are working to standardise the

use of GNSS. It is crucial to define requirements for professional high-accuracy sectors like

construction, agriculture and rail, as they have been early adopters of GNSS and their

applications are well-established. In terms of the operating environment, these sectors have

well-defined boundaries and controlled environments in which positioning tasks are carried

out. The potential for automation in these industries promises to deliver increases in

productivity, so the incentive is high for GNSS standardisation.

This chapter aims to review PNT user requirements in professional applications. The existing

frameworks used by the GNSS providers are presented as a reference to gather performance

requirement parameters broadly. Additionally, positioning specifications from different but

related industries like aviation, intelligent transport systems and telecommunications are

discussed as context to adapt specific requirements. Finally, a list of performance parameters

is proposed for professional applications in general and used to itemise applications in civil

construction, agriculture and rail transport.

5.1 PNT USER SEGMENTS

It is crucial to establish the professional high-accuracy user segment within the broader context

of PNT users. Different organisations classify PNT user segments depending on their

requirements and sector. For example, the broadest range of users is considered by the US DoD

in their National Positioning, Navigation, and Timing Architecture Study, which includes

military and homeland security. In contrast, the GSA takes into account only commercial and

civil users in its GNSS market reports. The civil construction, agriculture and rail applications

selected in this research are well-established GNSS users and are included in all these

document frameworks.

In the US, the National Security Space Office (currently DoD Executive Agency for Space

Staff) undertook a study to understand the existing ‘as-is’ architecture and recommend a future

‘should-be’ national PNT architecture (National Security Space Office, 2008). Its report

consolidates existing requirements and projected user group size (Figure 5-1) segmented into

domains (space, air, surface and sub-surface), which are further sub-divided into sectors

(military, homeland security, commercial and civil).

It is difficult to predict technology adoption, but the architecture study correctly identified the

dominance of large user groups in LBS, automotive and military, among others. For navigation

applications, the largest groups are in transportation, machine control and agriculture and the

commercial sector is larger than the other sectors combined. Since 2008, when the study was

published, new GNSS-dependent technologies have been introduced with massive adoption,

namely UAVs. UAVs have since been identified as a significant user base in commercial

market reports and are predicted to surpass traditional aviation as applications for construction

and agriculture. UAVs are also commonplace high-accuracy applications in construction and

agriculture.

Figure 5-1 PNT user perspectives 2025 (National Security Space Office, 2008)

The GSA publishes a several market reports (GSA, 2016, 2017, 2018a, 2018b, 2018c, 2018d,

2019a, 2019b) focused on commercial GNSS market trends and more specific user segments

with information on shipments, revenues and installed receiver base. Among the nine market

segments studied, road and LBS are predicted to continue dominating the GNSS market in

terms of revenue and installed base. The niche ‘professional’ segments are predicted to account

for 6.7% of cumulative revenue during 2019–2029 (Figure 5-2). The new development in this

market study is that UAVs are expected to comprise 56% of the installed receiver base for

professional markets (Figure 5-3). It could be argued then, that the commercial sector is being

driven by technological adoption of GNSS and new use cases in different market segments.

The importance and reliance of high-accuracy GNSS for professional industries are expected

to grow with these revenues and installed base predictions. For example, the agriculture,

surveying and infrastructure applications of UAVs make up 60% of the market’s value, so the

growth of the UAV market is closely tied to these industries. Within the professional segments,

the highest-accuracy sectors are agriculture and surveying, which make up over 50% of the

cumulative professional revenue and installed receiver base.

Figure 5-2 Cumulative revenue 2019–2029 of ‘professional’ segments (GSA, 2019a)

Figure 5-3 Installed base of ‘professional’ segments 2019–2029 (GSA, 2019a)

5.2 REVIEW OF PNT REQUIREMENTS

It is necessary to study relevant requirements frameworks from various levels to define

requirements for PNT applications in civil construction, agriculture and rail transport. At the

system level, GPS, GLONASS, Galileo, BeiDou, QZSS and IRNSS have published system

performance standards that outline the expected performance of their signal-in-space (SIS)

services. For end-users, the SIS approach does not consider error sources from UE and surface

environment. Hence, organisations like ICAO have designed PNT requirements frameworks

from the user level to meet the specific needs of the aviation industry.

In a similar approach, this research proposes a unified framework with 13 relevant performance

parameters selected from different sources to be applied to civil construction, agriculture and

rail transport. The sources consulted include system-level documents, such as the US, Russian

and European radionavigation plans. Also reviewed was the GSA user consultation platform,

which presents requirements for different industries. These documents reveal the importance

for GNSS operators and government agencies to study all sectors within a unified framework.

At the user-level, different industries have published their own requirements criteria, however

there are dissimilarities between frameworks. For example, in aviation, required navigation

performance (RNP) specifications consist of the requirements needed to support a navigation

application. Performance requirements in this context are based on four parameters: accuracy,

integrity, continuity and availability. In contrast, the rail industry has an existing framework

that has been adapted to GNSS and is focused on dependability and safety. The standard system

parameters are defined in terms of reliability, availability, maintainability and safety (RAMS).

For industries that are heavily regulated and standardised, such as aviation and rail, industry

bodies set requirements and promote the use of navigation technology in technical manuals or

standards. Other industries like construction and agriculture lack these standards. This section

discusses the shared characteristics of the requirements frameworks used in PNT, from the

highest level of national policy to specific system specifications and industry requirements.

5.2.1 Defining Performance Parameters for High-Accuracy Applications

From different requirements frameworks, several performance parameters can be defined in

this research for the context of professional applications. A detailed list of performance

parameters and sources is presented in Appendix B. The following list of parameters was

selected based on commonality across industries. For example, accuracy is shared across

several industries, while connectivity is adopted from the requirements framework specified

by the 3rd Generation Partnership Project (3GPP) for telecommunications. For professional

applications, a framework of performance parameters is proposed in this research as follows:

• Accuracy: a measure of position error; that is, the difference between the user’s

estimated position and the exact position. The measure is expressed as a horizontal and

a vertical component and with a probability of occurring generally defined as 95%

(ICAO, 2008).

• Latency: the time elapsed between the event that triggers the determination of the

position-related data and the availability of the position-related data at the positioning

system interface. At initialisation of the positioning system, the latency is also defined

as the time-to-first-fix (TTFF) (3GPP, 2018).

• TTFF: the time elapsed between the event triggering for the first time, the

determination of the position-related data and the availability of the position-related

data at the positioning system interface. TTFF is greater than or equal to latency (3GPP,

2018).

• Availability: the probability that a user is able to determine their position with the

specified accuracy and is able to monitor the integrity of their determined position at

the initiation of the intended operation (ICAO, 2008).

• Continuity: the probability that a user is able to determine their position with the

specified accuracy and is able to monitor the integrity of their determined position over

time. Assuming the service is available at the start of an operation, this is the probability

of it becoming unavailable over a specified time interval that is linked to the duration

of the operation (ICAO, 2008).

• Integrity: a measure of the trust that can be placed in the correctness of the position

solution; it includes the ability of a system to provide timely and valid alerts to the user.

The integrity risk is defined as the probability that a user will experience an error larger

than the horizontal alert limit (HAL) or vertical alert limit (VAL) without an alert being

raised within the specified time-to-alert (TTA) at any instant in time (ICAO, 2008).

• Robustness: a qualitative, rather than quantitative, parameter that depends on the type

of attack or interference the receiver is capable of mitigating. It can include

authentication information to assure users that the signal comes from a valid source,

enabling sensitive applications (GSA, 2019a).

• Resilience: defined as the ability of the system to use an alternative PNT source when

the primary source of PNT (typically GNSS) does not provide the required level of

accuracy, availability, integrity or continuity. Transition to a secondary PNT source

upon losing a primary PNT source must maintain safety, security, continuity and

minimal PNT service disruptions (SAE International, 2018).

• Coverage: the surface area or space volume in which the PNT service is adequate to

permit the user to determine position to a specified level of accuracy. Coverage is

defined as local or operational; regional; or Australia-wide and global (DoD et al.,

2017).

• Environment of use: the physical environment in which the user operates. It describes

the coverage area as well as the high-level properties affecting RF propagation and

positioning, such as the nature of the service area (e.g. open, meaning open sky or aerial;

or obstructed, meaning suburban, canyons urban or natural, indoor including tunnels).

In the case of multiple environments, the attribute should also define whether the use

case is expected to operate seamlessly in all these environments (3GPP, 2018).

• Connectivity: the need for a communication and/or connectivity link of an application

to be able to receive and communicate data to third parties. Connectivity involves long-

range communication technologies such as satellite, 3G and Long Term Evolution

(LTE), as well as short-range technologies such as Bluetooth and near-field

communication (NFC) (GSA, 2019a).

• Interoperability: the ability of different vehicle positioning systems with different

absolute positioning capacities to be used on the road network and still meet the

required relative positioning performance requirements. Meeting the interoperability

requirement demands that all vehicle positioning systems operate with some level of

minimum consistency in terms of positioning algorithms, hardware, signals and

infrastructure (Austroads, 2013).

• Traceability: A traceable measurement is one that can be related to national or

international standards using an unbroken chain of measurements, each of which has a

stated uncertainty (GSA, 2019a p. 107).

5.2.2 Radionavigation Plans

The US Federal Radionavigation Plan (DoD et al., 2017) is a document outlining PNT policy

and planning. It has been jointly published at least every 3 years since 2001 by the DoD,

Department of Transport (DoT) and DHS, together with other US government agencies. This

plan covers present and future federally funded PNT systems such as GPS and eLoran. The

objective of the reports is to ensure a robust mix of systems to meet user requirements in

different industries.

The latest Russian Radionavigation Plan (приказом Минпромторга России, 2019) includes

requirements from ICAO and IMO and defines industry requirements of national interest. Some

of the topics addressed are the need for ICAO certification by the SDCM SBAS and

interoperability between GLONASS, SDCM and other GNSS.

The EC published the European Radionavigation Plan (EC, 2018) with a focus on Galileo and

EGNOS to reduce dependence on GPS. Additional sections of this chapter discuss the

modernisation of PNT systems with an emphasis on robust backup systems like Iridium STL.

Other countries, including Sweden, have published their own radionavigation plans with a

focus on GNSS and dedicated ground infrastructure for augmentation and backup (Swedish

Maritime Administration, 2009). Australia currently does not have a radionavigation plan, but

it is expected there will be some form of national policy framework published with the

Australian SBAS capability together with the NPIC.

5.2.3 European GNSS Agency User Consultation Platform

The GSA user consultation platform was launched in December 2017 to understand GNSS user

needs before the Galileo FOC. The project aimed to produce comprehensive Galileo user

requirements documents for multiple sectors and user segments. The initial reports on user

needs and requirements were published in October 2018 (GSA, 2018b, 2018c, 2018d, 2019b).

Data input into the platform was undertaken in the form of technical discussion forums for each

user segment where specific requirements were defined and validated.

There are commonalities between the user consultation platform and the current research. The

GSA and Galileo, as a civilian use system, have a rationale for developing a user requirements

framework that covers all major user segments in a study and with a focus on European users.

This project shares a similar motivation—to propose a user requirements framework and

review requirement values. However, the scope of this study is within the context of specific

professional applications and focused on the Australian market. Similar efforts are being

undertaken to define user requirements contained to specific industries, with a more detailed

definition of applications, some of which are described in the following section.

5.2.4 Requirements Framework in Related Industries

User requirement parameters for PNT services, in general, can be defined as accuracy,

initialisation, repeatability, availability, integrity, timeliness, continuity and reliability (Feng et

al., 2009). There are specific industries where additional parameters or unique concerns must

be considered to represent end-user needs. It is essential to consider requirement frameworks

from other sectors to provide a perspective and gather requirements for the three case study

industries.

Aviation

The aviation industry is the most mature sector in terms of PNT requirements. In the 1990s,

the ICAO developed a framework for defining RNP to be implemented at a global level in the

Performance-based Navigation (PBN) Manual (ICAO, 2008). In this document, performance

requirements and navigation specifications are described for two types of flight operations: en-

route and approach. Standalone GNSS is recommended for en-route operations, where

positioning accuracies of 3.7–0.74 km are required. However, in approach operations, where

vertical positioning accuracies of up to 4 m are required, GBAS infrastructure is necessary.

The GNSS performance requirements for the different types of operation are presented in Table

5-1.

The framework used for these requirements is based on the Minimum Operational Performance

Specification (MOPS) standard defined by the RTCA. Accuracy values have been defined at

the 95% confidence level and separated into horizontal and vertical components. The integrity

is the probability of the system providing a solution within a HAL and VAL, and raising an

alarm within a TTA. The continuity is the probability of the system providing a solution with

the specified accuracy and integrity over time. The availability is the probability of the system

providing a solution within the accuracy and integrity at a single moment. These performance

Table 5-1 ICAO GNSS SIS performance requirements (ICAO, 2006)

parameters from aviation and other sectors are discussed in more detail in Section 5.2.4.

Accuracy (95%)

Typical operation

HAL

VAL TTA

Integrity

Continuity Availability

Horizontal Vertical

3.7 km

N/A

1–1x10-7/h

En-route (continental)

3.7– 7.4 km

5 min

0.99– 0.99999

En-route (terminal)

0.74 km

N/A

1.85 km N/A

15 s

1–1x10-7/h

0.99– 0.99999

220 m

N/A

556 m

N/A

10 s

1–1x10-7/h

Non-precision approach

0.99 0.99999

1–10x10-4/h to 1–10x10- 8/h 1–10x10-4/h to 1–10x10- 8/h 1–10x10-4/h to 1–10x10- 8/h

16 m

20 m

40 m

50 m

10 s

1–8x10-6 per 15 s

0.99 0.99999

16 m

8 m

40 m

20 m

6 s

1–8x10-6 per 15 s

0.99– 0.99999

Approach operations with vertical guidance Approach operations with vertical guidance

16 m

6–4 m

40 m

6 s

Category I precision approach

35– 10 m

1–8x10-6 per 15 s

0.99– 0.99999

1–2x10-7 in any approach 1–2x10-7 in any approach 1–2x10-7 in any approach

Cooperative Intelligent Transport Systems

Commercial automotive manufacturers are implementing C-ITS with positioning solutions that

meet their operational needs. However, no standard for positioning performance has been

defined or published. Extensive standardisation efforts in Europe are being led by the European

Telecommunications Standards Institute, the European Committee for Standardization and the

EC. Similar work is carried out in the US by the Institute of Electrical and Electronics

Engineers (IEEE) and SAE International, previously Society of Automotive Engineers (SAE).

Meanwhile, the ITU, the 3GPP, OneM2M and others are undertaking standardisation work

worldwide.

In Australia, Austroads is the organisation studying the implementation requirements for C-

ITS; it has acknowledged there were no uniform performance standards in the industry for

positioning requirements at the time of writing (Austroads, 2013). The positioning

requirements defined in the Vehicle Positioning for C-ITS in Australia report (Table 5-2) are

in the early stages of development, and further work is required. One key characteristic of the

transport industry is the additional requirement for relative positioning—that is, vehicle-to-

vehicle—where multiple independent user agents are interacting in the same environment. As

machine automation and fleets of robotic machinery expand into professional areas like civil

Table 5-2 Positioning requirements for C-ITS (Austroads, 2013)

and agriculture, there is a need to adopt a similar criterion for these applications.

Accuracy requirement

Research prototype

Type

Level

Communication latency (s)

95% confidence level (m)

RMS (order)

Road

5.0

metre

1–5

Lane

1.1

sub-metre

1–1.1

V2I: absolute

Where in lane

0.7

decimetre

0.1

Road

5.0

metre

0.1

Lane

1.5

sub-metre

0.1

V2V: relative

1.0

Where in lane

decimetre

0.01–0.1

In 2017, SAE International put together a PNT Technical Committee that would address the

need for a complementary PNT to protect critical infrastructure and automated vehicles. SAE

International published an initial positioning requirements framework in its SAE6857 standard

(SAE International, 2018). This document aims to define the technical requirements for a

terrestrial-based PNT system to improve vehicle (e.g. unmanned, aerial, ground, maritime)

positioning and navigation solutions, and to ensure critical infrastructure security by

complementing GNSS technologies.

Given the interdependence of positioning and communication in C-ITS applications, several

international organisations dedicated to telecommunications standards, including the ITU and

3GPP, have begun defining requirement specifications for future connected cars, IoT and LBS

over 5G. The most mature requirements framework comes from the 3GPP technical

documents.

Telecommunications

The new LTE generation 5G promises to be an essential driver for location-aware applications

like IoT, C-ITS and augmented reality, where positioning is integral to their design. Current

PNT solutions are not sufficiently accurate or robust to meet the requirements for emerging

applications. To address this, some hardware manufacturers in the LBS environment have been

advancing the use of raw GNSS signals from dual-frequency, multi-constellation GNSS chips

to improve performance in accuracy and integrity (North Coast Media LLC, 2016; 2018).

5G-enabled devices will also use a combination of sensors that can complement GNSS (Figure

5-4). Several proposals for a combined positioning framework are being developed and

incorporated into 5G specifications. Providers have already used GNSS data from mobile

devices specified in the 4G protocol; for example, assisted-GNSS is commonly used by device

manufacturers to improve TTFF and accuracy. New additions to the 5G protocol will include

dissemination of GNSS corrections like RTK and SSR to improve its accuracy. In this scenario,

connected 5G devices use a range of positioning technologies to provide an integrated solution

Figure 5-4 Positioning performance for 5G technologies in different environments (Fraunhofer IIS, 2018)

that is available outdoors and indoors.

The 3GPP is a standards group created to promote 3G mobile phone system specifications in

the telecommunication industry. While the name specifies 3G technology, the group also

maintains and develops standards for 4G and 5G LTE. Within the 3GPP framework, standards

are published as ‘releases’; the latest publication announced at the time of writing is Release

16, scheduled for late 2019. Work for this specification includes the Technical Report TR

22.872 (3GPP, 2018), a study on positioning use cases relevant to 5G devices. This technical

report analyses several use cases and identifies potential requirements for future 5G positioning

services. Table 5-3 summarises performance requirements of some relevant positioning use

Table 5-3 Sample of use case requirements for 5G positioning services (3GPP, 2018)

cases.

Potential requirements per use case

Update

Use case

Other key performance

Environment of use

Position accuracy Velocity Avail.

rate or

TTFF

Latency

indicators

interval

Enhanced positioning—

0.5 m horizontal

Trolley

99%

20 ms

outdoor/indoor

1–3 m vertical

1 m horizontal

Accurate

Outdoor

98%

10 s

5 s

0.3 m vertical

positioning

MCX confidence

for first

Event-triggered report

1 m horizontal

Indoor

95%

10 s

1 s

responders

2 m vertical

Traffic

5G positioning service area—

1–3 m horizontal

Anti-spoofing

monitoring

95%

10 Hz

10 s

30 ms

outdoor

2.5 m vertical

Anti-tampering

and control

5G positioning service area—

Anti-spoofing

Road user

outdoor

<1 m (across-track)

2 m/s

99%

1 Hz

10 s

Anti-tampering

charging

Enhanced positioning—

3 m (along track)

tunnels

5G positioning service area—

300 s–1

10–30 m horizontal

5 m/s

99%

20 mJ/fix (average)

outdoor

day

Anti-spoofing

Asset tracking

1 s in

Anti-tampering

and

enhanced

Enhanced positioning—

management

Support for ‘out of coverage’

1 m horizontal

99%

1 s

positioning

outdoor

positioning

area

Low energy

UAV

5G positioning service area—

0.1 m horizontal

0.5 m/s

Anti-spoofing

10 s

99%

(Data

outdoor

0.1 m vertical

2 deg.

Anti-tampering

analysis)

Anti-spoofing

5G positioning service area—

0.5 m horizontal

150 ms

99%

UAV

Anti-tampering

0.3 m vertical

outdoor

(Remote

Anti-spoofing

Enhanced positioning area—

0.5 m horizontal

control)

150 ms

99.9%

Anti-tampering

0.1 m vertical

outdoor

Support

5G positioning service area—

2 m horizontal

90%

10 s

1 s

multiple

outdoor

Management of different KPI*

different

and positioning services

Enhanced positioning area—

location

0.1 m horizontal

99%

10 s

1 s

indoor

service

* Key performance indicator

The 3GPP has identified GNSS as the only available sensor that can deliver better than 3 m

accuracy in outdoor environments. For high-accuracy applications (10 cm level), high-

accuracy GNSS is the only sensor that can meet the requirements within a 5G positioning

service area. Future work will be carried out to implement these potential requirements into a

normative requirements document as part of the final 5G specification.

5.3 PNT REQUIREMENTS IN CIVIL CONSTRUCTION

Civil construction is defined in this study as the discipline tasked with the creation of

infrastructures such as roads, railways, buildings, water reservoirs, subdivisions, airports,

bridges, sewer systems, tunnels and dams. Several construction tasks require some form of

positioning and navigation and are increasingly reliant on GNSS: surveying, automated

machine guidance (AMG), automated machine control (AMC), UAVs and asset tracking. This

section discusses the requirements for these applications, gathered from the literature and

industry practices.

For civil construction, there are no published standardised PNT solutions or positioning

requirements, as is the case for aviation. However, accuracy tolerances are specified in contract

documents and manuals. When a public or private client requests bids for a civil project, the

construction tolerances are defined in a contract, and it is up to contractors to select the best

technology available to match the specifications. In some cases, the positioning method is

recommended or required for some everyday operational tasks. For example, an increasing

number of projects is explicitly requesting the presence of GNSS machine guidance and control

for bulk earthworks using heavy machinery.

In recent decades, GNSS-reliant technologies like AMC, AMG and UAV have matured to

become widespread and commonplace in a broad range of civil applications. Similarly, there

is potential for new technologies like automation and connectivity to become ubiquitous, with

GNSS positioning as the backbone for its adoption. It is thus essential to gather general

specifications of the most common PNT applications for adoption across the industry.

5.3.1 Surveying

The primary users of GNSS in civil construction have traditionally been surveyors. Engineering

surveyors establish local reference frames and networks, survey the existing conditions of the

environment, set-out design models, guide construction, perform as-built surveys and monitor

structures after construction. The accuracy requirements for this user segment is at the

millimetre to centimetre level, and a combination of observation techniques, such as total

station measurements, laser scanning, static GNSS and GNSS RTK is used to perform these

tasks. Professional surveying organisations publish recommendations and guidelines for

applying each measurement technique, and it is up to the surveyor to select the correct tool for

each application.

Currently, surveyors and the construction industry are adopters of GNSS RTK as a

measurement method due to several factors. GNSS instruments are easier to use than traditional

survey tools. This means that construction workers can perform some survey tasks instead of

surveyors. GNSS is a more efficient measurement method and has more extensive coverage

because of the independence of optical LOS between ground marks. Given these reasons and

recent trends lowering barriers to adoption, the use of GNSS in construction is expected to

grow into new applications.

The technology improvements of GNSS and other instruments reflect the industry need for

availability and robustness. For example, several hardware manufacturers offer hybrid GNSS

and total stations that can carry out survey tasks in a combination of obstructed environments

where RTK outages are likely to occur because of cycle slips or multipath errors. Also, some

GNSS receivers combine RTK with PPP solutions to provide continuity during RTK

communication outages.

In this research, the surveying applications considered are only those tasks performed in the

discipline of construction surveying, mainly set-outs and simple surveys. The establishment of

control, deformation monitoring, and other specialised surveying tasks are not considered part

of the civil construction segment.

5.3.2 Automated Machine Control

For heavy machinery, AMG implies that an operator has a monitor displaying navigation

parameters to perform a task while manually controlling the machine or implement (bucket or

blade). The term AMC applies to the full control of the machine implement without operator

input. In most AMC systems used in construction, only the blade or implement are automated

while the operator oversees steering and driving. The elementary processes of construction,

where AMC has a central presence, are excavation, trenching, bulk earthmoving, grading,

trimming, compacting and paving.

These tasks have varying requirements for accuracy, and there is a general emphasis on vertical

accuracy and control (<5 cm). The tasks of trimming, fine grading and paving have high

positioning accuracy requirements (<2 cm) that cannot be performed by machine control with

GNSS alone. These tasks require complementary measurement instruments, like robotic total

station, laser, INS, tilt sensors or ultrasonic sensors, to provide an accurate solution.

The machine guidance can be achieved using either a 2D system or full 3D system. 2D systems,

which measure elevation and slope, use laser guidance to display elevation information and

enable manual machine control. Full 3D systems use GNSS to display the machine in relation

Figure 5-5 (a) Control screen of an excavator machine control system. (b) Excavator with 3D-GPS system. (c) Dozer with 3D-GPS system (Position Partners, 2020)

to a 3D design model and perform construction tasks with more elaborate designs (Figure 5-5).

Although it is technically possible to fully automate all elements of the machine, such as in

tele-remote operated and fully autonomous robots in mining (Australian Mining, 2017;

Billingsley et al., 2008; Corke et al., 2008), these are currently limited by the market and

complex nature of the construction environment. Future trends in research for machine

automation and robotics appear to implement semi-automatic construction machines (Vähä et

al., 2013) while integrating them with real-time telematics and information workflows in smart

construction sites (Kuenzel et al., 2015).

In the commercial environment, heavy equipment manufacturers (e.g. Komatsu, CAT, John

Deere) are moving away from the retrofitted after-market systems of machine control providers

(e.g. Topcon, Trimble, Leica) towards factory integration of machine control systems for semi-

automated machines with the integration of various sensing technologies (Bennink, 2015). This

demonstrates the pervasiveness of GNSS and machine control in the industry and the need for

interoperability and connectivity among end-users with equipment from different

manufacturers.

The demands of the civil construction industry have also led manufacturers to come up with

new GNSS equipment designs that improve on accuracy and robustness. One case is the

improvement of vertical accuracy for GNSS survey and machine control equipment.

Construction tolerances are often specified in separate horizontal and vertical components.

Millimetre-level vertical accuracies are unattainable by GNSS RTK. Levelling, total station

and laser levelling techniques meet the accuracy demands for vertical surveys, but they are

costlier and more complex to operate for multiple users on a construction site. For this reason,

some hardware manufacturers have combined optical and laser instruments with GNSS into

integrated equipment that offers the advantages of both methods (Figure 5-6). One example of

this is the Topcon Millimetre Laser system, which uses laser and GNSS for millimetre-level

Figure 5-6 (a) Motor-grader using automatic blade control with robotic total station for fine grading. (b) Tracked paver controlled by GNSS and Millimetre Laser system (Elneser Gonzalez, 2016)

vertical accuracies in machine control tasks (Topcon Positioning Systems, 2019).

5.3.3 UAVs

UAVs are robotic systems capable of operating pre-defined tasks in autonomous flight. The

market for commercial and professional UAVs offers off-the-shelf units with multiple cameras,

collision avoidance systems and GNSS RTK that are used for photogrammetry and visual

inspection. Applications include aerial surveys, quantity monitoring, site progress and

structural inspection.

Surveyors have widely adopted the use of UAVs, and they are now a standard tool for field

surveying. Photogrammetric and LiDAR surveys are a well-established technique for mass data

capture over large areas. However, their use is limited because of the high cost and planning

required to perform manned aerial flights. The availability of low-cost, easy-to-operate,

unmanned flying platforms has enabled the proliferation of aerial surveys for most surveying

firms. Depending on the terrain, UAVs can deliver 3-cm accuracy aerial surveys over several

hectares in hours, compared with weeks with traditional point measurement methods.

However, photogrammetry has limitations with heights, where, as a rule of thumb, the vertical

accuracy is three times the horizontal accuracy. To overcome this, some UAVs can carry

LiDAR payloads that offer millimetre-level accuracies over varying surfaces and vegetation

coverage.

The construction industry is using UAVs in non-survey operations for structural inspection and

site condition monitoring. These applications do not require accurate absolute positioning but

do require increased levels of situational awareness where the UAV must determine its position

relative to external objects for collision avoidance and flight planning. In these applications,

where the UAV is operating close to objects, the robustness of the positioning solution as a

whole is a necessary requirement.

5.3.4 Asset Tracking and Process Automation

Construction sites are busy, dynamic environments with heavy equipment, vehicles and

workers, and varying levels of engagement (project owners, equipment sub-contractors,

delivery drivers), sharing the same space. Automation in the construction industry is mainly

focused on automating processes and equipment by applying principles of industrial

automation to the construction sector. The aim is to improve productivity, safety and quality,

and reduce costs (Saidi et al., 2008).

The civil construction industry has shown progress in automation of construction processes,

civil integrated management, augmented reality, telematics and asset and personnel tracking

on the job site (Balaguer & Abderrahim, 2008; Heikkilä, et al., 2014). With the availability of

low-cost GNSS and 4G devices, fleet tracking of vehicles, plant and assets is a mature market.

Tracking provides asset location for monitoring, scheduling and logistics in process

automation. There is an increasing focus on real-time asset tracking of equipment, personnel

and collision detection systems to prevent accidents and improve safety. New technologies in

IoT and 5G promise to extend the application of tracking to objects and materials in the

construction environment.

An example application of fleet tracking and process automation is the operation of asphalt

paving in road construction. This is a complex operation that involves haul trucks loading

material from an asphalt plant and delivering the material to site at the right temperature and

moisture levels. The material quantity must match the productivity rate of graders and pavers,

which lay the material with centimetre accuracy on the surface. Later, compactors roll the

material to a specified thickness and hardness. This delicate balance of timing and technical

operations is the task of construction supervisors who are interested in avoiding costly delays

and optimising the utilisation of material and equipment. This has motivated manufacturers to

provide software and hardware solutions that help supervisors control this complex operation.

These solutions rely on GNSS fleet tracking for haul trucks and process automation for

machine interactions.

5.3.5 Gathering PNT User Requirements for Civil Construction

The civil construction industry has no standardised requirements for PNT, and it is challenging

to implement standards for positioning applications given the dynamic nature of large-scale

civil project environments. Some progress in standardisation has come from road authorities,

project owners and construction contractors, who have begun to implement specifications for

the use of AMC technology in construction projects. However, there is duplication and

conflicting information, as different organisations publish their own specifications manuals for

road construction projects.

The Oregon, Iowa and other states DoT offices in the US have studied the application of AMC

to improve automation in the construction workflow and proposed requirements for

construction. The Iowa DoT published a set of best practice rules in an implementation manual

for 3D modelling and AMC, and Hammad et al. (2012) reviewed the state of practice of AMC

in this and various other DoTs in the US. Meanwhile, in Australia, the Department of Transport

and Main Roads for Queensland specifies the construction tolerances for road projects shown

in Table 5-4. These values have been collected from the construction of main roads technical

specifications (MRTS) manuals: MRTS-04, MRTS-05, MRTS07A, and MRTS07C (State of

Table 5-4 Summary of construction tolerances from MRTS manuals

Queensland, 2018; 2019a; 2019b; 2019c).

Minimum tolerance (95%)

Application

Horizontal

Vertical

MRTS04 - Clearing and grubbing

1 m

MRTS04 – Excavation

5 cm

2.5 cm

MRTS04 - Bulk earthworks

5 cm

2.5 cm

MRTS05 - Fine grading of unbound pavement surfaces

5 cm

1.5 cm

MRTS07A - Fine grading of In situ stabilised subgrades using quicklime or

5 cm

1.5 cm

hydrated lime

MRTS07C - Fine Grading of in situ stabilised pavement using foamed bitumen

5 cm

−0.5–1.0 cm

It is important to note that as more civil construction projects request AMC as a mandatory

requirement for equipment working on-site, there is a need to define positioning standards to

ensure construction tolerances are met. In the example of the MRTS04 specifications (refer to

Table 5-4) for bulk earthworks, 95% vertical tolerance of 2.5 cm is achievable with RTK

machine control under ideal conditions but presents a problem in real-world obstructed

environments and at longer baseline distances.

Equipment sub-contractors, surveyors and engineers are aware of current limitations and make

decisions to implement complementary positioning technologies when higher accuracy is

required. For AMC grading, vertical performance cannot be achieved by GNSS alone and must

be augmented with complementary technologies.

UAVs are used in the construction industry mainly as survey tools for generating digital terrain

models (DTMs), site surveys and stockpile measurements. For tasks where the vertical

accuracy requirement is within 10 cm, UAVs can be used to perform preliminary, or rough,

surveys reliably. For surface measurements that require below 3-cm accuracy, conventional

survey techniques must be used. However, some construction companies are combining both

techniques and accepting the trade-offs in accuracy and efficiency. For example, the cost of

excess material resulting from calculation errors in UAV DTM surveys is offset by the cost

savings in productivity increase.

For this part of the study, multiple specifications manuals from Australian road authorities and

DoTs in the US were reviewed. Based on the available published construction tolerances from

different organisations in Australia and other countries, a table of accuracy requirements was

populated. With this preliminary information and industry practices of designers and

contractors, it was possible to validate specific use cases, and further information was input

into the requirements framework for civil construction. The proposed requirements table is

shown in Table 5-5. Further validation and continuous review are required from industry to

confirm and maintain these requirements.

Table 5-5 GNSS user requirements for civil construction

Measures of minimum performance criteria to meet requirements

Accuracy (95%)

Integrity

Application

Robustness Resilience Coverage

Connectivity Interoperability Traceability

Latency (s)

(TTFF) (s)

Availability (%)

Continuity (%)

Environment of use

Horizontal (m)

Vertical (m)

TTA (s)

Alert limit (m)

Surveying—manual survey, set-out

0.02

0.03

0.1

99.99%

N/A

Optional

Australia

Obstructed

Optional

Mandatory

AMC—excavation

0.02

0.03

0.1

99.99%

0.1

Desirable

Desirable Operational Obstructed

Desirable

0.02

0.05

0.1

99.99%

0.1

Desirable

Desirable Operational

Open

Desirable

Mandatory

UAV—site conditions

0.1

0.2

0.1

99.99%

0.3

Desirable

Desirable Operational

Open

Desirable

Mandatory

UAV—site inspection

0.1

99.99%

Desirable

Desirable Operational Obstructed

Desirable

Mandatory

N/A

99.5%

N/A

Australia

Obstructed

Mandatory

Optional

Tracking & Process Automation—fleet tracking

N/A

99.5%

N/A

Australia

Obstructed

Mandatory

Optional

Tracking & Process Automation — asset tracking

5.4 PNT REQUIREMENTS IN AGRICULTURE

The growing demand for food production has been driving the optimisation of the agriculture

industry and the development of new technologies. The most evident advances have come from

the use of machines, which have allowed farmers to work on large areas of land in a uniform

way to maintain sustainable production. However, newer sensing technologies can measure

information about individual crops within a field to treat different conditions accordingly. This

shift from a ‘uniform’ to a ‘differential’ treatment approach enables better decision making,

further increasing yields and reducing environmental impacts.

Modern farm management combines crop health information at the specific plant or row-level

with GNSS-guided machinery into the precision agriculture farming management approach or

site-specific crop management. Several technologies underpin the concept of precision

agriculture, including remote sensing and geographic information systems, but GNSS is the

fundamental enabler of its application in practice. Generally, GNSS in precision agriculture is

used for geotagged-data capture, guidance and control of machines, and more recently, UAVs

and robotics.

Equipment manufacturers and commercial providers have been delivering fit-for-purpose

GNSS augmentation for machines, UAV and other applications in agriculture. Farmers use the

available technology solutions to solve problems with the precision agriculture approach on a

case-by-case basis. For example, seeding and variable-rate application (VRA) require

machines to guide an implement over specific crop areas less than 10 cm wide. This is achieved

with AMC and GNSS RTK augmentation, but other applications may not need such high

accuracies. There are a few unified requirements frameworks that include all PNT applications

in agriculture. This section aims to define agricultural activities and gather existing information

about their PNT requirements.

5.4.1 AMC for Precision Agriculture

As with civil construction applications, AMG and AMC have been successfully applied to

heavy machinery for agriculture operations. Guidance and auto-steer systems have redefined

modern farming techniques by allowing precise repeat passes of farming equipment over rows

of crops that, when applied to a whole farm, resemble traffic lanes. This is often referred to as

controlled traffic farming (CTF). GNSS guidance can be applied to tractors, self-propelled

sprayers, combines and towed implements to perform tasks of planting, seeding, spraying,

spreading and harvesting. CTF makes farming operations more efficient, maximises land use

and reduces the impact of machinery on the soil.

The use of GNSS machine guidance in CTF has also facilitated new farming practices with

improved productivity, such as strip-till, no-till, drip-tape irrigation and mixed crops.

Equipment manufacturers have begun providing machinery and tractor implements specifically

designed for these tasks with GNSS systems as part of their factory-fitted equipment (Figure

Figure 5-7 Active GNSS guidance of a seeding machine implement (Heege, 2013)

5-7).

The techniques of inter-row seeding and spraying, where row separation is 10 cm, can only be

achieved with high-accuracy GNSS RTK (Whelan, 2007). Farms commonly operate local

GNSS reference stations or access NRTK subscription services by commercial or government

agencies. However, machine guidance in agriculture is also a core use case for PPP subscription

services where decimetre-level accuracies and 30-minute convergence times are sufficient for

several applications.

Several sensing techniques can be applied to precision farming. One of the most common

applications is yield monitoring, which offers the ability to continuously map yield with the

use of sensors mounted on a combine during harvest (Figure 5-8). The sensor data can be

geotagged to map yield and observe its spatial variability over time. Yield-monitoring systems

were initially developed in the 1990s and retrofitted to combine harvesters. They are now

commonplace and are integrated into the machinery from the factory. Yield-monitoring

systems log data at sub-metre accuracy with DGNSS. However, as new driverless combines

emerge in the market, the centimetre-positioning solution required for the guidance component

can be made available to the yield component integrated in the tractor.

Figure 5-8 Cotton yield monitor with optical flow sensors and DGNSS (Perez-Ruiz & Upadhyaya, 2012)

Another application of precision agriculture using machine control is VRA, where the amount

of material (fertiliser, weed control) sprayed is based on the location within the field. For

example, in the scenario of diseased crops, VRA allows for precise application of fumigant to

a specific plant, reducing waste and effects on healthy crops. Additional applications can be

carried out for weed control, fertiliser, lime and seed. During the process of application, the

variable control can be ‘prescribed’ by a map previously collected manually in the field, by

UAV or determined in real-time by sensors onboard the machine arms.

5.4.2 UAVs

Satellite imaging and manned photogrammetry are well established in agriculture for

generating maps from visible and multi-spectral sensors. These maps deliver products such as

soil and crop monitoring, disease detection, moisture content, plant counting, biomass

measurements, terrain and irrigation maps for land management and animal tracking for

livestock management. With the popularisation of UAVs, farmers have more accessible

information obtained at a lower cost and with higher temporal and spatial resolution.

One clear advantage of UAV technology is its ability to produce high-resolution images of

around 3-cm pixel size (depending on altitude and sensor), which can be georeferenced with

centimetre-accuracy control points or with GNSS RTK in real-time. The downside of UAV

aerial surveys, compared with manned or satellite, is in the coverage. Most fixed-wing

platforms have flight times of around 1 hour and can legally be flown within LOS. This means

that UAVs are best suited to small farms with an area of operation of around 200 Ha. This last

limitation can be extended by applying for operational exceptions to remote pilot licences that

include beyond-LOS flights and using long-endurance platforms.

Apart from remote sensing applications, UAVs can be used as robotic equipment for

agricultural tasks. Some multi-rotor platforms can carry small tanks of material for localised,

variable-rate spraying using a similar approach as VRA from automated tractors. This benefits

viticulture and other densely planted crops where tractors and spraying implements are

challenging to manoeuvre. This mode of operation requires the UAV to fly at a constant height

above crops with centimetre accuracy.

5.4.3 Livestock and Asset Tracking

Several applications in agriculture, especially in livestock, require tracking in real-time or over

delayed intervals. The main uses are for monitoring herds, managing grazing and identifying

patterns that indicate an individual animal’s health. Tracking systems for livestock employ

GNSS collars that transmit data in near-real-time so that farmers can track their herds on a

mobile device app. These collars have long battery life and use autonomous GNSS.

Asset tracking allows farmers to locate and better utilise valuable equipment and can provide

improved decision making and logistics when applied across a farm as a ‘connected farm’

vision. This application is closely linked with telemetry and IoT to generate data, including

location, of several elements of a farm. The requirements of these applications are not for

accuracy but for connectivity and low power consumption.

5.4.4 Robotic Applications in Agriculture

The introduction of semi-automatic systems in combine harvesters was one of the first steps

towards automation in agriculture. Now, fully automated tractors and machines are

incorporated into agriculture tasks from harvesting to the intelligent application of herbicides

(Edan et al., 2009).

One of the main drivers for automation is the high labour costs and shortages in some countries.

A significant disadvantage of having large and heavy automated machines is the potential for

accidents when interacting with humans and other objects. The current approach to automation

focuses on fleets of smaller machines that are deployed simultaneously and work cooperatively

with much less risk and lower cost.

Agriculture is an ideal environment to develop fully autonomous land or aerial vehicles capable

of operating in fleets or swarms while carrying out dedicated tasks with minimal supervision.

One advantage in agriculture is that the operating space is shared with few external agents.

Thus, automated machinery has few interactions with other objects and persons. Automation

is an attractive approach to operations in small-scale settings.

Europe’s Robot Fleets for Highly Effective Agriculture and Forestry Management project is an

example of the application of a fleet of agricultural land and aerial robots for effective weed

management (EC, 2014). Other robot systems for fruit picking, pruning, weeding, spraying and

Figure 5-9 RIPPA™, the Robot for Intelligent Perception and Precision Application (Australian Centre for Field Robotics, 2018)

monitoring are being developed and commercialised as demonstrated in Australia (Figure 5-9).

5.4.5 Gathering PNT User Requirements for Agriculture

Accuracy requirements can be defined in agriculture applications on a case-by-case basis.

Other requirements, like continuity and availability, are not well defined even though they have

been identified in the literature (Figure 5-10). It can be assumed that as robotic applications

increase, so too will the need for continuity and availability.

As an example, the Australian Centre for Field Robotics operates several robotic platforms

with decimetre to centimetre accuracy requirements currently achieved with commercial L-

band PPP and RTK. As the interaction and sharing of information between platforms increases,

there is a greater need for interoperability and connectivity. Based on the current research and

industry trends, the framework and use case requirements for agriculture have been identified,

as shown in Table 5-6. Further validation and continuous review are required from industry to

confirm and maintain these requirements.

Table 5-6 GNSS user requirements for agriculture

Measures of minimum performance criteria to meet requirements

Accuracy (95%)

Integrity

Application

Robustness Resilience Coverage

Connectivity Interoperability Traceability

Latency (s)

(TTFF) (s)

Availability (%)

Continuity (%)

Environment of use

Horizontal (m)

Vertical (m)

TTA (s)

Alert limit (m)

0.1

N/A

0.1

99.95%

0.3

Desirable

Desirable Operational

Open

Desirable

Mandatory

AMC—listing, bedding, ridging, tillage, discing

0.02

0.1

99.95%

Desirable

Desirable Operational

Open

Desirable

Mandatory

AMC—land forming

0.02

0.1

99.95%

0.1

Desirable

Desirable Operational

Open

Desirable

Mandatory

Monitoring

Other applications

Applications

Farm machinery guidance

Automatic steering, VAR

Harvest/yield monitoring, biomass monitoring, soil sampling

Accuracy (sub-metre) Availability

Accuracy (sub-metre) Availability Continuity

Accuracy (sub-decimetre) Availability Continuity

Livestock tracking, virtual fencing, geo- traceability, machinery monitoring, field boundary measurements Accuracy (sub-metre) Availability Authentication

Key GNSS requirements

Connectivity

Connectivity Interoperability

Connectivity Interoperability Traceability

Other requirements

Figure 5-10 Key requirements in agriculture. Adapted from GSA (2019a)

5.5 PNT REQUIREMENTS IN RAIL TRANSPORT

The rail transport industry is a frequent user segment in different PNT requirement documents.

Applications of PNT in the rail industry can be divided into three areas: safety-related,

operational (mass commercial/information and management) and professional applications

(infrastructure and civil engineering). Augmented GNSS is currently being used in different

applications for operational and professional purposes such as track surveys and asset tracking.

However, safety-related applications have been slow to adopt it. Nonetheless, European rail

operators have identified GNSS as a required sensor for future train management systems and

work towards certification and implementation is being carried out, as discussed below.

5.5.1 Safety Applications in Rail Transport

Train positioning and supervision of train movement are essential safety-related applications

that have conventionally been done using signalling systems along a track. Traditional

signalling systems in rail transport enable safe train movements by routing trains onto sections

of track (blocks) and maintaining enough space to avoid collisions while optimising line

capacity. New implementations of signalling systems make use of automatic train operation,

automatic train protection (ATP) and automatic train control (ATC) approaches that rely on

dedicated signalling infrastructure. The combination of these concepts with GNSS allows for

a communications-based train control (CBTC) system using moving blocks and reduced

infrastructure. The ERTMS is proposing an implementation of a new ATC system using

EGNOS, Galileo and GPS.

In a CBTC system, continuous positioning and communication allow for the minimum space

between two trains following each other to be reduced to the braking distance of the second. In

this scenario, safety, reliability and line capacity are increased but are highly dependent on the

accuracy and integrity of the position and speed measurements. Improvement of GNSS

accuracy and integrity in rail applications have been widely researched (Beugin et al., 2018;

Kubo et al., 2015; Lu et al., 2018).

However, GNSS presents several problems in the rail environment. The main one is signal

obstruction in dense vegetation and tunnels, which can degrade the positioning accuracy and

availability. Thus, GNSS must provide integrity parameters that account for this performance

degradation. Another factor is the stringent certification and regulatory framework within the

rail industry, which makes adoption of GNSS for satefy-related applications a slow process.

Nonetheless, several projects in Europe, including InteGRail, ERTMS on Satellite, and the

Railway High Integrity Navigation Overlay System, have addressed this in the context of the

ERTMS. With significant policy efforts, the European rail industry is likely to adopt EGNOS,

Galileo and GPS for ATC and other safety-related applications.

ATC is the primary safety application that benefits from the use of GNSS, where the absolute

position of a train or rolling stock must be located within the width of the smallest track (1 m).

The relative position between two trains must also be known onboard trains and to increase the

efficiency of ATC. In the case of level crossing protection, an automatic level crossing system

must know the position and speed of trains to trigger barriers with minimum traffic delays. The

use of hybrid positioning systems such as odometer, inertial and GNSS, are used to provide a

robust solution in challenging operating environments with multipath, interference and signal

blockages (Figure 5-11).

Traditional ATCS—that is, non-GNSS—are highly automated management systems for rail

transport. By incorporating GNSS and better communication systems, there is potential to

improve ATCS through the reduction of accidents, delays and operating costs while improving

capacity and operating efficiency. This is evident in two Australian examples involving Rio

Tinto and the Australian Rail Track Corporation (ARTC).

In the remote Pilbara region of Australia, Rio Tinto’s AutoHaul project operates a fully

driverless train monitored via GNSS and a satellite communication link. The system has

increased productivity by reducing the loss-of-run time spent while drivers change shifts. This

was the world’s first fully autonomous implementation of a heavy-haul rail network as reported

by Rio Tinto (2018).

Figure 5-11 Examples of challenging operating environments: (a) Tracks with 3-m separation. (b) Tunnels and obstructions. (c) High multipath and interference areas (Sabina et al., 2018)

AutoHaul is an example of a private corporation improving its operational efficiency on a track

owned and controlled by its mining operation. The positioning accuracy and integrity

requirements are not the most stringent due to the remoteness of the track and because there is

little to no risk of interaction with external agents. This also poses a reduced safety requirement,

even though safety is improved by eliminating the risk of driver fatigue. In contrast,

government departments or statutory corporations operating medium and high-density rail

corridors have much higher accuracy, integrity and safety requirements.

This is the case for the ARTC, which is implementing its Advanced Train Management System

(ATMS) based on GNSS and inertial monitoring systems. The system was designed to provide

positioning accuracy of 2 m with high reliability and safety integrity, based on a commercial

WADGNSS service. A PNT solution is delivered to a central control system to improve

efficiency, safety and capacity (ARTC Ltd., 2019).

The technical feasibility of GNSS for rail safety-related application has been widely established

in research and in practice for several countries, including Australia. One of the biggest

challenges remaining for its implementation is the interoperability, policy and regulatory

framework. In Europe, progress in regulating the use of GNSS is underway in the context of

the ERTMS. In Australia, more international and intergovernmental collaboration is required

before GNSS can be adopted in rail safety applications. However, GNSS is currently used for

operational and professional applications.

5.5.2 Operational Applications in Rail Transport

GNSS is currently applied to several non-safety-related applications for rail management.

Some of the most common operational and logistic applications include fleet tracking and

passenger information systems.

Efficient tracking of trains and rolling stock is an essential factor to increase the capacity and

use of rail systems. Trains can be optimised by driving at an appropriate speed and reducing

braking to minimise maintenance and fuel costs. Fleet-tracked assets transmit data to a control

centre on a dedicated ground radio link or via satellite communication. There is reliance on

connectivity in these applications. The accuracy requirements in tracking systems are less

demanding than for safety-critical applications like ATC. Accuracy is in the order of tens of

metres; however, this is difficult to achieve by GNSS alone in obstructed environments. For

this reason, hybrid positioning systems are used in the on-board units as tracking platforms.

For passenger rail networks, there is a need to provide the public with accurate timetables for

train services. Passenger information systems are location and telematics solutions with an

emphasis on real-time information to reflect delays and time of arrivals and departures of trains.

The primary consideration is in the latency of the data transmission as it can have the most

significant effect on estimated times. Standalone GNSS is integrated into modern passenger

systems as the requirement for accuracy is relatively low; that is, in the order of 100 m.

5.5.3 Professional Applications in Rail Transport

Professional applications include data collection, surveying and machine control for

maintenance and engineering. The operation of ATC relies on knowledge of track geometry to

assign maximum speed limits to specific blocks. High-accuracy surveying techniques like

mobile mapping using LiDAR, GNSS RTK and inertial are used to determine the accurate

geometry of the railway corridors. Additionally, some implementations use mobile mapping

units onboard operating trains to provide real-time monitoring and change detection.

Track and geotechnical monitoring are also performed using GNSS and other sensors with

millimetre-level accuracies in post-processed mode. This level of accuracy requires the use of

GNSS reference stations along the rail corridor, in which some track operators have invested.

During repair and maintenance, GNSS reference stations are also used for RTK surveys and

machine control tasks related to engineering and construction. The existing GNSS

infrastructure can be used for rail operations as NRTK. These services are the most suitable

solutions for precise train positioning systems in the future expansion of railway networks

(Albrecht et al., 2013).

5.5.4 Gathering PNT User Requirements for Rail Transport

Positioning requirements for the rail industry must be quantified in terms of RAMS. Any

system implementation within a rail environment, such as communication, signalling or GNSS,

must comply with the RAMS framework defined in the standards EN 50126-1, EN 50128 and

EN 50129 (CENELEC, 2017; 2011; 2018). For example, the integrity concept used by ICAO

in the GNSS requirement framework for aviation can be translated to the safety integrity level

(SIL) concept in the RAMS framework.

A relationship between these two requirements frameworks can be established to translate

between the different parameters (Figure 5-12). Work is being carried out by the GSA to define

the required railway MOPS that will make them understandable by the GNSS community

(Marais et al., 2017). This policy and regulatory framework is under development in Europe to

allow the implementation of EGNOS and Galileo in safety-related applications. The adoption

of a GNSS-based ATC system that meets the regulatory framework for is being proposed by

Figure 5-12 GNSS and railway performance requirement comparison (Lu & Schnieder, 2014)

the ERTMS for Europe (Neri, Rispoli & Salvatori, 2015).

Requirements studies in rail have the additional problem of defining performance criteria

across challenging environments not present in aviation. The availability, continuity and

accuracy parameters can be degraded in environments such as tunnels and dense vegetation.

Also, the accuracy, latency and integrity parameters depend on the density and speed of tracks

and the interoperability between different operators. This makes the definition of user

requirements for rail a complex and iterative process that involves input from government and

railway operators.

The rail industry in Europe is one of the most advanced in defining user requirements and has

been working on adopting GNSS since the development of EGNOS. The GNSS rail user forum

segmented requirements into safety, operational and professional applications (Wiss et al.,

2000). The most recent GSA report on user needs and requirements incorporated additional

application categories, qualitative performance criteria, and yet-un-validated use cases (GSA,

2018c). The summary of rail GNSS user requirements from the GSA is shown in Table 5-7.

The GSA rail requirements shows qualitative measures for availability and integrity as a first

approximation. It is expected that more precise definitions are published in follow-up versions

of the document. The implementation of compliant, GNSS-based locator units onboard trains

will require the definition of these quantitative measures. It is likely that rail industries in

different countries follow a similar approach to implement GNSS for safety-related

applications. It is possible also, that the requirements framework from the European rail

industry is proposed as a standard to follow, so that European equipment manufacturers can

easily export their technology.

Some applications in the Australian rail industry have similar requirements frameworks as

those in Europe. ATC, traffic management and tracking are in varying levels of adoption in

passenger and freight rail networks. One of the more advanced systems is the ATMS

implemented on several remote lines across Australia (ARTC Ltd., 2019). It is technically

possible that the current system could be expanded with further low-and medium-density rail

networks. However, further policy and regulatory work is required to implement its use across

the sector.

With these considerations, along with the list of rail applications discussed previously, a

requirements matrix was populated. The requirement parameters were gathered based on the

literature review as shown in Table 5-8. Further validation and continuous review are required

from industry to confirm and maintain these requirements.

Table 5-7 Rail GNSS User Requirements. Adapted from GSA (2018b)

Requirement

Application

Accuracy (95%) Availability

Integrity

SIL

TTA1

Measures of minimum performance criteria to meet requirements

Integrity

Accuracy (95%)

Application

Robustness Resilience Coverage

Connectivity Interoperability Traceability

Latency (s)

(TTFF) (s)

Availability (%)

Continuity (%)

Environment of use

TTA (s)

Horizontal (m)

0.01

N/A

0.01

99.99%

Desirable

Desirable Operational Obstructed

Mandatory

Operational—dispatching

N/A

0.01

99.99%

125

Desirable

Desirable Operational Obstructed

Mandatory

Operational—passenger information

100

N/A

0.01

95%

N/A

Operational Obstructed

Mandatory

Professional—positioning of machines

0.02

0.03

0.01

99.99%

0.1

Desirable

Desirable Operational Obstructed

Mandatory

Professional—infrastructure surveys

0.02

0.03

0.01

99.99%

0.1

Desirable

Desirable Operational Obstructed

Desirable

Mandatory

Professional—fix point applications

0.02

0.03

0.01

99.99%

0.1

Desirable

Desirable Operational Obstructed

Desirable

Mandatory

100

5.6 SUMMARY

This chapter answered the final research question by presenting a user requirement study that

was proposed as a project outcome. This study has examined the positioning requirements for

user segments in different domains, from the system level to RNP frameworks and industry

standards. Several performance parameters and evaluation criteria have been adopted from

these standards to form a new requirements framework proposed in this research. This

framework was used to gather the PNT requirements for professional high-accuracy

applications.

Three key industries were selected in this research work: civil construction, agriculture and

rail. Within each industry sector, the most critical applications were identified, and their

performance parameters tabulated from a review of publicly available sources and industry

knowledge. This included how current technology is applied to positioning solutions and how

a future GNSS position service might contribute to its improvement. A matrix of user

requirements was presented to inform professional users on how to adopt an appropriate GNSS

augmentation service. The requirements matrix should be further validated by industry and

continuously updated.

From the tabulated requirements, it is evident that several low-accuracy applications in civil

construction may benefit from open-access to high-accuracy positioning in the form of PPP

and NRTK services. More demanding applications require local RTK or need to be

complemented with alternative positioning techniques to meet their high-accuracy

specifications. In agriculture, PPP and NRTK can meet the accuracy requirements of most

applications and tasks and open-access services will benefit this cost-sensitive sector. For rail

applications, while accuracy is useful for professional and operational applications, additional

parameters such as integrity, robustness and resilience are mandatory to meet the stringent

requirements of safety applications. Furthermore, the adoption of GNSS for safety-related rail

applications must be supported by a policy and regulatory framework.

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CHAPTER 6: EVALUATION OF PNT SERVICES

FOR PROFESSIONAL HIGH-ACCURACY

APPLICATIONS

102

6 EVALUATION OF PNT SERVICES FOR

PROFESSIONAL HIGH-ACCURACY APPLICATIONS

This chapter describes three applications that have been identified as potentially benefitting

from a national high-accuracy positioning service. The aim of the field evaluations described

here was to determine if modern satellite-delivered positioning solutions can meet the

requirements of the case studies.

The evaluations focus on parameters such as accuracy, availability, convergence time and

stability, as defined in the requirements framework discussed in Chapter 5. The accuracy is

determined by the RMS error between an evaluated solution and a ground truth solution such

as RTK or post-processed kinematic. The availability is calculated as the percentage of time a

solution is available during a test session.

Convergence time is the time required for a float PPP solution to reach a steady state. This state

is defined herein by an RMS below 10 cm for 10 minutes (Seepersad, 2012). PPP-AR solutions

achieve a fixed state when ambiguities are resolved. However, errors greater than 10 cm occur

in the high multipath environments of the case studies. The stability of the solution is measured

as the percentage of positions within an expected tolerance to quantify these errors.

The three applications selected have varying accuracy requirements. For the bulk earthmoving

application in the civil test, the specified vertical tolerance is 10 cm, and a PPP-AR solution

was used. For the robotic spraying application in the agriculture test, the specified horizontal

tolerance is 10 cm, and a PPP-AR solution was used. For the application of track monitoring

in the rail test, the specified horizontal tolerance is 1 m, and an SBAS solution was used.

6.1 CIVIL CONSTRUCTION AND MINING CASE STUDY

Bulk earthmoving is a common task carried out by human, machine operators in civil

construction, mining and agriculture. It consists of bulldozers stripping and pushing layers of

material such as topsoil, rock or coal from the ground, to move them onto stockpiles or prepare

the ground for precise landforming. This task is of interest to manufacturers and earthmovers

because of its potential to be fully automated. In civil construction, where job sites are

congested, most operations are partially automated on the blade while humans drive the dozers.

103

However, in some mine sites, where machines are more isolated than in construction sites,

there are examples of fully autonomous or tele-operated dozers with no humans on board.

This application requires vertical positioning accuracies of around 10 cm and is mostly carried

out using RTK positioning. The experiment was designed to validate the use of PPP-AR

solutions as an alternative to RTK. Because the accuracy of current PPP-AR solutions is around

10 cm, the focus of the evaluation will be the performance on the vertical component.

6.1.1 Experiment Design

The isolated environment of a mine site was deemed practical and safe to carry out the tests.

The selected test site was Energy Australia’s Yallourn Mine, which has several AMC dozers

performing routine dozer push operations. The equipment and operating facilities were made

available in August 2016 through a collaboration between FrontierSI, RMIT University,

Energy Australia and Position Partners (Elneser et al., 2017).

Two research PPP-AR and PPP+Ion solutions, developed by RMIT University, were

transmitted over satellite L-band using the QZSS LEX signal described in Section 2.2.2.1

(Harima et al., 2015). QZSS LEX was only available for 12 hours of the day during which the

QZSS-1 satellite was in view. A traditional RTK solution with around 2-cm accuracy was

transmitted over mobile Internet and used as ground truth.

For the PPP+Ion solution, local PPP-based ionospheric delays for GPS and GLONASS were

generated from a sparse network of 11 CORS (Figure 6-1) and transmitted over NTRIP to

calculate the real-time solutions (Harima et al., 2016). The average inter-station spacing in the

Yallourn Mine, Victoria

Figure 6-1 Stations used to calculate ionospheric delays. Location of field trials, Yallourn Mine, Victoria

CORS network was 66 km, and the minimum distance between any two stations was 31 km.

104

An independent positioning system was installed for the PPP-AR and PPP+Ion solutions to

minimise disruption and interference with the dozer. In this system, the primary GNSS antenna

was split into two test receivers, a Topcon Hiper M and a Javad Delta-3 with QZSS LEX

decoder. Figure 6-2 shows the primary and test equipment installed on the dozer.

e c a

Figure 6-2 a) CAT D11R dozer. b) Dozer GPS antenna. c) Existing and test GNSS receivers. d) Dozer guidance system. e) Test receivers and signal splitter. f) Tablet with RTKLIB (Takasu, 2013).

f b d

In push operations, the dozer proceeds forward from the top of a batter to a feeding point below

and then reverses back to repeat the operation. The testing was performed over three sessions

during which the dozer was operating under real conditions with varying scenarios of

obstructions, batter slopes, and operating speed. A summary of the sessions is shown in Table

6-1. An example of the ground track and position plot covered by the dozer during one of the

Table 6-1 Test sessions for real-time dozer positioning

tests is provided in Figure 6-3.

Test

Local Date/time

Duration (hr)

Real-time solutions

1

1:00

18-Aug-16 03:00

2

2:50

30-Aug-16 21:00

3

2:00

01-Sept-16 00:00

CLK91 PPP-AR CLK91 PPP+Ion RTK CLK91 PPP-AR CLK91 PPP+Ion RTK CLK91 PPP-AR CLK91 PPP+Ion RTK

105

Top of batter

Direction of push

Bottom of batter

101680

101660

101640

101620

E-W(m)

101600

101580

N-S(m)

Top of batter

101560 17600 17580 17560 17540 17520 17500 17480 17460 17440 875

870

865

U-D(m)

Bottom of batter

860

855

850

845

Figure 6-3 Dozer ground track and local East-West, North-South, Up-Down position plot for testing.

6.1.2 Performance Evaluation

The performance of the solutions is shown as an error time series in Figure 6-4 to Figure 6-6.

For Test 1, the convergence for the PPP-AR solutions was 21 minutes for the horizontal

component and 29 minutes for the vertical component. For PPP+Ion, the convergence time was

within 1 minute for both horizontal and vertical components (Figure 6-4). One noticeable

pattern of the graph is the accuracy shifts when the dozer works alongside the batter. In ideal

environments and static PPP tests, convergence time graphs show an asymptotic curve. In this

scenario, the challenging environment of the dozer on steep batters demonstrate biases in the

PPP-AR solution. However, the PPP+Ion solution proves more accurate.

106

Figure 6-4 Performance of Internet-delivered solutions for Test 1

For Test 2, the convergence time for PPP-AR was 76 minutes for the horizontal component

and 19 minutes for the vertical component. For PPP+Ion, the convergence time was 22 minutes

for the horizontal component and 55 minutes for the vertical component (Figure 6-5). There is

a weaker performance in this test demonstrated by the increasing horizontal error of the PPP-

AR solution as well as the higher than expected convergence time on both solutions. This can

potentially be attributed to the session starting while the dozer was moving along the steep

slopes of the batter. There is also a noticeable shift in the PPP-AR solution around 1 hour and

15 minutes caused by a wrong fix. This makes evident the need for an external estimation of

accuracy or integrity rather than users relying on the fixed status of the PPP algorithm.

For Test 3, the PPP-AR convergence time was 39 minutes for the horizontal component and

12 minutes for the vertical component. For PPP+Ion, the convergence time was within 1 minute

for both horizontal and vertical components (Figure 6-6). This test shows a similar performance

to test 1, with horizontal and vertical shifts in the PPP-AR solution and a stable PPP+Ion

solution.

107

Figure 6-5 Performance of Internet-delivered solutions for Test 2

Figure 6-6 Performance of Internet-delivered solutions for Test 3

Table 6-2 shows the convergence time for the horizontal and vertical components. The fix-rate

is shown as the percentage of ambiguity fixed solutions for the duration of the session. The

108

error is displayed as RMS after convergence. Finally, the stability of the solutions is shown as

a percentage of positions within 10 cm after convergence.

As anticipated, the convergence time of PPP+Ion was noticeably better than that of PPP-AR in

Test 1 and 3. In Test 2, however, the convergence time was higher than expected, although

within the tolerance of previous studies (Harima et al., 2016). The fix-rate was also better for

the PPP+Ion solution in two of the sessions. For Test 1, fix-rate was improved from 70.7% to

99.1%. In Test 2, where the convergence times were longer, the improvement was from 43.5%

to 57.4%. For Test 3, there was a decrease from 73.9% to 70.0%.

The accuracy and stability were better for the PPP+Ion than for the PPP-AR solutions in all

sessions. The accuracy of both solutions was within the vertical tolerance of 10 cm. However,

Table 6-2 Convergence time and performance of test sessions

the stability was lower for the PPP-AR solutions.

Convergence time

RMS (m)

Stability (%)

Real-time

Fix-rate

(hh:mm:ss)

Test

solution

(%)

Horizontal

Vertical

Horizontal Vertical Horizontal Vertical

00:32:32

00:29:01

70.7

0.072

0.049

100

99.5

PPP-AR

1

00:00:23

00:00:19

99.1

0.035

0.033

100

99.7

PPP+Ion

01:16:34

00:19:25

43.5

0.073

0.065

81.9

95.3

PPP-AR

2

00:22:22

00:55:00

57.4

0.039

0.049

99.0

98.8

PPP+Ion

00:39:21

00:12:10

73.9

0.057

0.053

94.7

93.0

PPP-AR

3

00:00:43

70.0

0.038

0.029

99.8

PPP+Ion

The open sky environment of the mine allows for a stable PPP+Ion positioning solution after

convergence even for moving machinery. However, in Test 2, some performance degradation

can be seen when the dozer was deep in the steep slopes that block part of the sky and change

the satellite geometry. The current PPP+Ion implementation is susceptible to these changes and

generates steps or incorrect fixes during the kinematic run. In the other sessions, where the

dozer was stationary for 20 minutes during the start of the operator’s shift, the solution could

converge to a better position fix. It is expected that in obstructed environments, this effect will

be exacerbated.

While the PPP+Ion sub-decimetre solution was stable up to 99.8% of the time, it still showed

some sessions of extended convergence and varying accuracy. Currently, commercial

providers are improving the performance of PPP and RTK techniques. It is possible that a

combined SSR-RTK solution could meet the accuracy and TTFF requirements of tasks like

109

bulk earthmoving. Until these improved solutions are available, RTK is likely to remain the

preferred method for AMC.

6.2 AGRICULTURE CASE STUDY

The agriculture industry is a significant user of PPP solutions delivered over satellite L-band

by commercial providers offering a range of subscription service. The limited Internet

connectivity and CORS coverage of some farming regions have contributed to this uptake.

Also, the accuracy of some farming applications is suited for sub-metre and decimetre

accuracies. However, there is an increasing demand for applications with the sub-decimetre

performance offered by PPP-AR and SSR-RTK. Subscriptions to these services, which are a

considerable cost for farmers, are feasible for a small number of tractors. However, the use of

them is not scalable to emerging applications where large fleets of smaller robots carry out

independent tasks.

Robotic agriculture is a new application in farming where autonomous robots carry out pre-

defined tasks with little or no human supervision. For example, in the task of spraying, robotic

tractors follow pre-defined paths along rows to detect and treat individual weeds. This task

needs decimetre accuracy and is currently done with RTK, as the more cost-efficient and

accurate solution. There is potential to use PPP-AR for decimetre-level positioning if the cost

reduces. This experiment was designed to demonstrate the use of PPP-AR as a positioning

technique to replace or augment RTK in robotic agriculture applications. A commercial PPP-

AR solution was used in place of a public access PPP signal, which is not yet available in

commercial receivers.

6.2.1 Experiment Design

This experiment proposed to install a test GNSS receiver on a robotic tractor carrying out weed

spraying on a farm. The equipment and operating facilities were made available in November

2019 through a collaboration between SwarmFarm Robotics and Position Partners. The

location of the field trials, in the SwarmFarm Robotics test fields (SwarmFarm Robotics, 2019)

near the town of Gindie, Queensland is shown in Figure 6-10.

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SwarmFarm Robotic field near Gindie, Queensland

Figure 6-7 Location of field trials, SwarmFarm Robotics, Gindie, Queensland

The field tests were carried out over 2 hours on the robotic tractor. An initial 20 minutes was

set to achieve AR while the tractor was stationary. Following this period, the robot entered its

pre-defined path along the sowing rows spaced at 12 m but was restarted because of a sensor

check 5 minutes into the routine. During this outage, there were no simultaneous data between

the RTK and the PPP-AR solutions.

The existing positioning equipment on the robot was a Topcon AGI-4 working with a local

RTK correction received over UHF. The test equipment was a Hemisphere S321 with an Atlas

H10 correction received over L-band. The Atlas H10 is a PPP-AR correction signal for GPS,

GLONASS and BeiDou constellations with 8-cm horizontal accuracy at the 95% confidence

interval level as specified in the service comparison in Chapter 4, Table 4-3. The robotic tractor

and GNSS test equipment are shown in Figure 6-8.

The RTK positions were logged on the Topcon AGI-4 receiver and raw data was post-

processed against a CORS in Minerva, Queensland, provided by Smartnet Australia (Hexagon

AB, 2019). Because the farm’s RTK corrections are on a local datum, a transformation was

applied to the tractor’s positions during post-processing. The PPP-AR positions were logged

on the tablet. The distance between the Hemisphere s321 and Topcon AGI-4 antennas was

applied as an offset along the direction of travel.

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Figure 6-8 Robotic tractor with GNSS test equipment

6.2.2 Performance Evaluation

The performance of the Atlas H10 PPP-AR signal was analysed in the horizontal component

only; the vertical is not required for this spraying application. The plot in Figure 6-9 shows the

horizontal error of the Atlas H10 solution compared with the post-processed kinematic used as

ground truth with an estimated 2.5-cm horizontal accuracy. The solution is required to perform

within the 10-cm error threshold for this application.

The horizontal error plot shows the solution converging during the 20-minute static period. At

1,216 seconds (20 minutes), the solution stabilised down to the 10-cm level. After this period,

the solution should be in a steady-state. In this state, the Hemisphere S321 receiver achieved a

fixed solution at 1,517 seconds (25 minutes) and maintained this fix for 99.4% of the test

duration. The solution entered a steady state shortly after, at 1,549 seconds, meaning it met the

strict threshold criteria of RMS lower than 10 cm for 10 minutes. However, the RMS error

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surpasses the accuracy threshold several times after 2,882 seconds (48 minutes). Once the

Figure 6-9 Atlas H10 horizontal error for robotic tractor test; 10-cm error threshold in red

The performance of the solution is detailed in Table 6-3. This shows the convergence time,

solution converged, the RMS throughout the kinematic run was 5.5 cm.

defined as horizontal accuracy below 10 cm for more than 10 minutes. The fix-rate is the

percentage of fixed PPP solutions computed by the Hemisphere S321 receiver during the run.

The error is displayed as RMS after convergence. The stability of the solutions is shown as a

Table 6-3 Horizontal performance of Atlas H10 solution for kinematic run on board a tractor

percentage of positions within 10 cm after convergence.

Convergence time (hh:mm:ss)

Fix-rate (%)

RMS (m)

Stability (%)

00:25:17

84.2

0.055

99.4

The 10-cm horizontal accuracy performance of Atlas H10 PPP-AR solution in this test is

comparable to the published parameters of the service provider. This is also similar to other

commercial solutions in the market and published specifications for future public access

signals. However, there could be some errors attributed to the accuracy and methodology of

obtaining the ground truth solution, so this should not be interpreted as a formal validation of

the Atlas H10 signal quality.

6.3 RAIL TRANSPORT CASE STUDY

GNSS is used for tracking trains and other rolling stock along remote, suburban and urban

railways lines. In remote areas, it is possible to use standalone GNSS to locate a train along a

track definitively. However, suburban and urban areas have denser railway corridors that

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contain multiple parallel tracks with spacing as close as 3 m. In these lines, it is necessary to

use a sub-metre-accurate GNSS solution to avoid positioning a train on the wrong track. This

experiment was designed to demonstrate the potential of a WADGNSS and SBAS solution to

provide sub-metre positioning on a suburban railway track in Tasmania, Australia.

6.3.1 Experiment Design

This experiment proposed to install a test GNSS receiver on a locomotive operating on the

TasRail Derwent Valley narrow gauge (1067 mm) line. The line runs between the towns of

Boyer and Bridgewater in Tasmania. These field tests were done in parallel with the SBAS

Testbed project (Geoscience Australia, 2019b). The equipment and operating facilities were

made available in April 2018 through a collaboration between FrontierSI, TasRail, Institute of

Derwent Valley Line, Boyer Tasmania

Figure 6-10 Location of field trials, TasRail’s Derwent Valley line, Tasmania

Rail Technology and Position Partners. The location of the field trials is shown in Figure 6-10.

A TR class locomotive running on the Derwent Valley line was used for the tests. The existing

tracking system uses a standalone Garmin GPS-only receiver that does not provide sufficient

accuracy and is heavily affected by tree coverage and obstructions along the line. The proposed

test equipment was a Trimble SPS461 with an OmniSTAR VBS signal, a Septentrio AsteRx-

U to record raw data for post-processing as ground truth, and a GMV SRX-10 to receive the

114

Australian single-frequency (L1) SBAS signal. The test equipment was installed on the roof of

Figure 6-11 TR11 locomotive and GNSS equipment installed for testing

the TR11 locomotive, as shown in Figure 6-11.

The test equipment was connected to the locomotive’s power system so that it would power on

and initialise recording as soon as the locomotive was turned on. The run between the Boyer

and Bridgewater depots took 1 hour, during which time data were collected. The data from two

test sessions recorded in April 2018 were analysed as described in Table 6-4. During the data

collection, the power equipment connected to the Trimble receiver experienced a failure and

the OmniSTAR data could not be recorded. The comparison was made only between post-

processed kinematic GNSS and the single-frequency (L1) SBAS.

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Table 6-4 Test sessions for the locomotive test

Test

Date/time Duration (hr)

Equipment and solutions

1

24-Apr-18

1:10

Trimble SPS461 OmniSTAR VBS (unavailable)

00:00

GMV SRX-10 Australian SBAS L1

Septentrio AsteRx-U (post-processed kinematic)

2

27-Apr-18

1:08

Trimble SPS461 OmniSTAR VBS (unavailable)

00:00

GMV SRX-10 Australian SBAS L1

Septentrio AsteRx-U (post-processed kinematic)

6.3.2 Performance Evaluation

The performance of the positioning solution was analysed in terms of horizontal accuracy,

availability and stability. Figure 6-12 shows the error plots for the two test runs on board the

locomotive. The start of the runs was time synchronised by their departure location so that the

time series represent the same location along the shared tracks.

For Test 1, the plots show accuracies around 10 cm at the start of the run. Some losses of lock

can be seen where obstructions completely block satellite visibility. The accuracy slowly

deteriorates to metre level around 1,500 seconds, where the vegetation becomes denser around

the rail line. Even under vegetation canopy, the performance of the solution was maintained

below 1 m for most of the time. It is essential to highlight that the post-processed kinematic

Vegetation coverage

Outages caused by obstructions

Vegetation coverage

Outages caused by obstructions

Figure 6-12 Horizontal error of the Australian SBAS signal for the locomotive Test 1 (red) and Test 2 (yellow).

solution used as ground truth maintained a fix throughout the section under vegetation canopy.

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In Test 2, the locomotive started on a different track before joining the line travelled for Test 1

at 1,440 seconds. The same accuracy shift can be seen between 1,500 and 3,000 seconds. Figure

6-13 shows some examples of the outages caused by obstructions at the beginning of the run

Test 1 with Time (s) of run Test 2 with Time (s) of run

Start of vegetation coverage

Outages caused by obstructions

Test 1 with Time (s) of run Test 2 with Time (s) of run

Section of vegetation coverage surrounding track

Figure 6-13 Track of TR11 run with obstructions and vegetation canopy

and the vegetation canopy covering the line at 1,500 seconds into the run.

The performance of the Australian SBAS signal during the test runs was within the

application’s required tolerance of 1 m, with some outliers. These correspond to periods under

dense canopy, where the accuracies degraded to outside the tolerance in some cases. Table 6-5

shows the performance parameters analysed in these tests. The error is presented as RMS. The

availability was calculated as a percentage of the time a position was available during the run.

The stability was calculated as a percentage of positions within 1 m.

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Table 6-5 Performance of Australian SBAS signal during locomotive tests

Test

RMS (m)

Availability (%)

Stability (%)

1

0.387

99.0

99.3

2

0.459

98.1

95.5

6.4 SUMMARY

In this chapter, three case studies were selected to validate the performance of various satellite-

delivered positioning solutions. This completes project outcome 3 and demonstrates some of

the user requirements specified in Chapter 5.

Bulk earthmoving in civil construction and mining requires 10-cm vertical accuracies and was

tested using an experimental PPP-AR and SSR-RTK solution delivered over QZSS LEX.

Robotic spraying in agriculture requires 10-cm horizontal accuracies and was tested using a

commercially available PPP-AR signal. Train tracking along parallel lines requires 1-m

horizontal accuracy and was tested using the preliminary Australian single-frequency L1 SBAS

testbed.

Professional applications requiring accuracies below 10 cm, which include bulk earthmoving

and robotic spraying, use RTK as the most cost-effective and accurate positioning solution.

The test results demonstrated that it is not practical to use PPP-AR to replace or augment RTK

in these scenarios, because of the long convergence time and accuracies exceeding the

threshold at times. However, SSR-RTK can meet TTFF and accuracy requirements and is a

viable solution provided the costs of accessing these signals are lowered.

Commercial providers are continually improving their PPP-AR and SSR-RTK solutions.

Accuracies for some of the SSR-RTK correction signals presented in Chapter 4 are within 5

cm and convergence times are below 2 minutes. The experimental PPP+Ion signal transmitted

over QZSS LEX demonstrated the potential of solutions complementary to the commercial

providers.

For positioning applications in the rail industry, SBAS and PPP solutions can provide the

required sub-metre performance in clear sky conditions. However, in obstructed sections of

track, the positions can easily degrade to over 1-m accuracy. Because of the strict safety

requirements in rail, this demonstrates the need for real-time integrity information and resilient

equipment design that includes inertial or similar additional sensors.

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CHAPTER 7: CONCLUSIONS AND

RECOMMENDATIONS

119

7 CONCLUSIONS AND RECOMMENDATIONS

This thesis has presented a study of industry PNT requirements to be used by the Australian

NPIC. The focus was on professional industries including civil construction, agriculture and

rail. The first phase of the research provided an overview of positioning methods, infrastructure

and services available for GNSS augmentation. The main body of research outlined PNT

requirements criteria and evaluated a subset of applications in three key professional user

segments. This chapter summarises the main findings from this work and recommends future

actions.

7.1 CONCLUSIONS

The research findings can be summarised into two categories. The first relates to the positioning

methods, infrastructure and products expected from a national high-accuracy GNSS

positioning service. The arguments in this section are summarised from Chapters 2, 3 and 4

and answer the initial research questions:

1. What is a national high-accuracy GNSS positioning service?

2. What are the current gaps in infrastructure and services for a GNSS positioning service?

The second category relates to specific requirements in civil construction, agriculture and rail

transport as a subset of professional high-accuracy user segments. This was expressed in

Chapter 5 and validated in Chapter 6 to answer the third research question:

3. What are the user requirements of professional applications in civil construction,

precision agriculture and rail?

7.1.1 Positioning Methods, Infrastructure and Products

Users of a national high-accuracy positioning service expect RTK accuracy at low cost. RTK

and PPP are the most popular GNSS augmentation techniques for professional users, with each

offering unique advantages and disadvantages. RTK is currently the only GNSS solution for

centimetre-level applications, and it has been widely adopted in the survey, civil construction

and agriculture industries. PPP is also well established for professional markets in remote areas,

where GNSS infrastructure is deficient, and connectivity is not guaranteed. Commercial PPP

solutions are aimed mainly at marine and agriculture segments, although applications are

increasing in rail, mining, survey and construction with the advent of SSR-RTK.

120

With GNSS hardware becoming increasingly accessible, it is necessary to re-evaluate the

GNSS augmentation services marketplace. Currently, users can select from a range of GNSS

augmentation services with varying levels of accuracy, from the metre to centimetre level. Most

of these are costly, subscription-based services aimed at the high-end professional markets. It

is unlikely that future mass-market and non-professional users will be able to access these

services under current pricing models. Also, as the new public GNSS, RNSS and SBAS-

generated PPP services become available, the market may be disrupted. Therefore, new service

providers must offer cost-competitive solutions to mass-market and non-professional users.

It is possible, however, for public PPP providers and commercial providers to coexist by

focusing on niche markets, B2B and performance parameters. Commercial providers have

differentiated their services by offering sophisticated and integrated solutions for SSR-RTK,

with advanced compression algorithms, integrity information, authentication and the option to

integrate into third-party devices. Likely, demanding applications in professional segments that

require a level of service and support will continue to use similar products.

In RTK, commercial providers add value to public CORS by providing increased infrastructure

density and networked solutions. Still, some professional users operate their own base

infrastructure and RTK corrections because of subscription services not meeting their

performance and cost needs. This has led to a demand for short-baseline user-owned RTK

services. Additional options are offered to users who require service-level guarantees, support,

telemetry, analytics and ease of use.

Users of a national high-accuracy positioning service expect improved positioning by

combining GNSS with complementary sensors. Although augmented GNSS is one of the most

versatile positioning methods available, it has well-known limitations. Performance parameters

can be degraded in terms of coverage, where connectivity is not available; accuracy, for

applications that need precision below 5 cm; and continuity, when operating in obstructed or

indoor environments.

There is a trend for equipment manufacturers to integrate GNSS with other technologies (e.g.

INS, imaging, ranging) in a sensor fusion approach to improving GNSS performance for

specific applications. The professional markets have indicated increasing adoption of high-

accuracy GNSS services. As sensor fusion matures, it is expected that the adoption of high-end

applications will use these methods, particularly those tied to robotics, machine automation

and connectivity.

121

7.1.2 User Requirements of Professional Industries

PNT services are a vital enabler for automation and connectivity in professional user segments.

Some industries, such as telecommunications and C-ITS are adopting GNSS positioning and

developing standards and requirements for PNT services. Within this context, a requirements

framework was developed in this thesis for applications in three key professional industries:

civil construction, agriculture and rail transport.

Current and future PNT applications were identified for each industry as part of a literature

review and through consultation with key industry representatives. Some applications like

AMC in construction and agriculture are quite mature and have well-known requirements. New

and proposed applications, particularly in robotics, UAVs and automation are still being

defined, so the performance requirements of available technologies are not yet clear.

A PNT user requirements framework was presented based on 13 performance parameters:

accuracy, integrity, latency, TTFF, availability, continuity, robustness, resilience, coverage,

environment of use, connectivity, interoperability and traceability. Some parameter values are

clearly identified but others need further consultation with industry to be resolved. The

outcome of the requirements study is presented in three tables (Table 5.5, 5.6 and 5.8), that

identify the minimum performance of a PNT solution. The requirements should be further

validated and continuously updated by industry consultation.

Some applications in civil construction require accuracies below 1.5 cm in the vertical

component and must use GNSS-complementary methods like laser and optical measurements.

Most AMC, UAV and other robotic platforms currently use RTK to meet the accuracy

requirements of their diverse applications. However, there is a growing need for integrity,

robustness and connectivity as added-value services. Low-accuracy applications like real-time

fleet and asset tracking require connectivity as a primary parameter.

In agriculture, AMC is the main application for PNT and accuracies vary from metre to

centimetre level depending on the task being carried out. However, as manufacturers develop

fully autonomous tractors, it will be possible to integrate centimetre-accuracy positioning

solutions from the navigation to blade implements on the tractor. There are several robotic

applications requiring decimetre accuracy that can be achieved with PPP services. These robots

rely on vision and other sensors to carry out their specific farming tasks, and it is possible to

fuse these sensors into a single positioning solution. Most of the AMC, UAV and robotic

122

applications would benefit from additional integrity information, robustness and connectivity

as is the case for civil construction applications.

The rail industry is a slow adopter of GNSS because of the certification framework needed for

safety applications. However, several operational and professional applications are

increasingly adopting RTK for high-accuracy positioning. For CBTC, it is possible to use

SBAS and PPP to achieve accuracy requirements. There are also other important parameters,

like integrity and robustness that need to be considered. Some commercial and private rail

operators have already implemented GNSS solutions in their platforms, as done by ARTC. For

public operators, however, it is currently a challenge to establish interoperable systems between

rail networks. Therefore, further work is required within the industry to establish the policy

and regulatory framework needed to increase GNSS adoption in safety-related applications.

7.2 RECOMMENDATIONS

Several countries have published radionavigation plans that include user requirements, system

definition, risks and opportunities of the PNT environment. Australia does not yet have its own

PNT capability. However, if an SBAS in the region is deployed, there should be consideration

given by the government to include a national policy framework in the form of a

radionavigation plan or a user consultation platform. This will allow stakeholders to provide

viewpoints relevant to the positioning needs of the nation.

Professional users and SoL applications require robust and assured positioning. The first can

be delivered by hardware manufacturers using multi-sensor data fusion. The second must be

delivered at a system level, using integrity parameters and spoofing mitigation techniques. For

example, Galileo includes encrypted signals and authentication of services for selected users

and applications. It is possible to extend similar services to wider regions including Australia.

These can be accessed through partnerships with public or private users. One opportunity for

commercial partnership might be a fee to operate or access these services. Commercial

companies could apply for a licence to operate the authenticated signals and make them

available to the industrial needs that will benefit from this increased security.

Safety-related rail applications require integrity and robustness parameters to fully implement

GNSS. While this may be technically feasible, further engagement with rail stakeholders is

needed to design the policy and regulatory framework for its implementation, as is being done

by the ERTMS.

123

This research has presented a review of various applications in three professional industry

sectors. Continuous engagement with industry is recommended to maintain requirements

updated. However, other sectors and user segments were not considered; thus, future work is

required to maintain a record of current user needs. Also, a related body of work is being carried

out by the GSA in its user consultation platform to promote the use of Galileo services to meet

end-user application requirements. Further work is also needed to unify these frameworks in

different sectors and to maintain and update requirements for each end-user application.

124

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144

Appendix A ACCURACY MEASURES

To effectively compare different measurement techniques and applications, it is necessary to

understand various accuracy definitions and express them in a standard measure. In statistics,

accuracy is defined as the closeness of a measurement to its true (generally unknown) value.

Precision describes the closeness of measurements to their mean (repeatability). A commonly

cited example of the relationship between accuracy and precision in two dimensions is

Figure A-1 Relationship between accuracy and precision. Adapted from van Diggelen, 2007.

For a random variable 𝑥 and a density function of its distribution 𝑓(𝑥), the mean 𝜇, standard

illustrated in Figure A-1.

deviation 𝜎 and variance 𝜎2 are given by (Hofmann-Wellenhof et al., 2003):

+∞ −∞

, (1) 𝑥 𝑓(𝑥)𝑑𝑥 𝜇 = ∫

+∞ −∞

(2) (𝑥 − 𝜇)2 𝑓(𝑥)𝑑𝑥 𝜎 = √∫

By standardising into a random variable 𝑧:

(3) 𝑧 = (𝑥 − 𝜇) 𝜎⁄

𝑧2 2

The probability density function of a normal Gaussian distribution is:

√2𝜋

(4) 𝑓(𝑧) = 𝑒−

145

The probability 𝑃1 for an interval (−𝛼, 𝛼) is:

+∞ −∞

(5) 𝑒−𝑧2 2⁄ 𝑑𝑧 𝑓(𝑧)𝑑𝑧 = ∫ 𝑃1(−α < z < α) = ∫

The value for the error probability of a normal Gaussian distribution can be expressed using

different measures of accuracy depending on the application. Table A-1 shows different

notations expressing the distribution of errors according to their probability and standard

Table A-1 One-dimensional accuracy measures for Gaussian distribution

deviation range, while Figure A-2 shows the probability in a Gaussian distribution graph.

Probability (%)

Notation

𝜶

0.67

50.0

Linear error probable

1.00

68.3

1σ level or RMS

1.96

95.0

95% confidence level

2.00

95.4

2σ level

3.00

99.7

3σ level

Figure A-2 Accuracy measures in a one-dimensional Gaussian probability distribution. Adapted from van Diggelen, 2007.

For 2D space, where two independent random variables 𝑥, 𝑦 represent a horizontal position,

the error distribution is defined by a standard error ellipse. Table A-2 shows the different

notations used to express an error distribution in two dimensions, while Figure A-3 shows the

spatial relationship between three error ellipses at 50%, 68& and 95% probability.

146

Table A-2 Two-dimensional accuracy measures for a Gaussian distribution

Probability (%)

Notation

√𝜶

39.4

1σ or standard ellipse

1.00

50.0

Circular error probable

1.18

63.2

Distance RMS

√2

86.5

2σ ellipse

2.00

95.0

95% confidence level

2.45

98.2

2DRMS

2√2

98.9

3σ ellipse

3.00

Figure A-3 Accuracy measures in a two-dimensional Gaussian probability distribution. Adapted from van Diggelen, 2007.

Similarly, in 3D space, the error distribution is defined by a standard error ellipsoid. Table A-3

Table A-3 Three-dimensional accuracy measures for a Gaussian distribution

presents the notations for expressing an error distribution in three dimensions.

Probability (%)

Notation

√𝜶

1.00

19.9

1σ or standard ellipsoid

1.53

50.0

Spherical error probable

61.0

Mean radial spherical error

√3

2.00

73.8

2σ ellipsoid

2.80

95.0

95% confidence level

3.00

97.1

3σ ellipsoid

Given the different measures for expressing position accuracies in 1D, 2D and 3D space, a

relationship can be identified among these metrics and used to convert between estimates.

Table A-4 displays the interrelationships between some standard accuracy measures. CEP is

the 2D 50% circular distribution, RMS1 is the RMS in one dimension, RMS2 is RMS in two

147

dimensions corresponding to 63.2%, and the percentages are horizontal accuracy distributions

Table A-4 Interrelationships among accuracy measures for a Gaussian distribution

in circles containing 67%, 95%, 68% and 98% of the points in a sample (van Diggelen, 2007).

CEP

RMS1D

RMS2D

67%

98%

95%

68%

1.26

2.08

1.28

2.37

CEP

0.85

1.19

1.49

2.45

1.51

2.80

RMS1D

1.41

1.06

1.74

1.07

1.99

RMS2D

1.64

1.01

1.88

67%

0.62

1.14

95%

1.85

68%

98%

148

Appendix B PERFORMANCE PARAMETERS

The primary performance requirements standard in most industries, specifications and

applications are (as defined by the ICAO):

• Accuracy is a measure of the position error; that is, the difference between the estimated

and exact positions, which will be experienced by a user with a certain probability at

any instant in time. In general, the probability used for accuracy requirements is defined

as 95%.

• Integrity is a measure of the trust that can be placed in the correctness of the position

solution. Integrity includes the ability of a system to provide timely and valid alerts to

the user. The integrity risk is defined as the probability that a user will experience an

error larger than the HAL or VAL without an alert being raised within the specified

TTA at any instant in time.

• Continuity is defined as the probability that a user is able to determine its position with

the specified accuracy and to monitor the integrity of its determined position over the

time interval applicable for the corresponding phase of flight. Assuming the service is

available at the start of operation; this is the probability of it becoming unavailable over

a specified time interval that is linked to the duration of the operation.

• Availability is defined as the probability that a user is able to determine its position with

the specified accuracy and is able to monitor the integrity of its determined position at

the initiation of the intended operation.

In the case of the automotive sector, the SAE has designated additional performance

requirements for autonomous vehicles and UAV applications. In this context, the operating

environment is considered, multi-sensor data fusion is assumed, and there is an increased focus

on assurance and security. In the telecommunications sector, the 3GPP has included

requirement parameters for applications of LBS, autonomous vehicles and IoT where power

and privacy requirements of UE are discussed. For these frameworks, some additional general

requirements may need to be considered.

Accuracy can be broken down into the following six concepts (3GPP, 2018) depending on the

application, and several measurement techniques can be used to derive different components

of the PNT parameters in a multi-sensor environment:

149

• Horizontal position accuracy is a measure of the relationship between the UE/vehicle

and ground truth positions. The location accuracy can describe either an absolute

position accuracy or of relative position accuracy.

• Vertical position accuracy is a measure of the relationship between the UE/vehicle and

ground truth altitudes in an absolute or relative reference frame.

• Speed accuracy describes the closeness of the measured magnitude of the UE’s velocity

to the true magnitude of the UE’s velocity.

• Bearing accuracy describes the closeness of the measured bearing of the UE to its true

bearing. Both the measured and true bearings are defined in a common base coordinate

system using yaw-pitch-roll as for aircraft principal axes.

• Velocity accuracy describes the closeness of the measured velocity of the UE to its true

velocity vector for a moving UE; the velocity is the combination of the heading and the

speed.

• Timestamp accuracy relates to position-related data (e.g. position, velocity), which are

usually associated with a timestamp marking the time when position-related data have

been determined. The timestamp accuracy describes the closeness of the timestamp

value to the true instant when the related data were computed.

The parameters of availability and continuity can be expanded into latency (some documents

refer to timeliness) and TTFF for applications that require high accuracy during operation and

are very sensitive to latency but can wait for an initial convergence time of minutes. The

definitions of these parameters are as follows (3GPP, 2018):

• TTFF: time elapsed between the event triggering for the first time the determination of

the position-related data and the availability of the position-related data at the

positioning system interface; TTFF is greater than or equal to latency.

• Latency: time elapsed between the event that triggers the determination of the position-

related data and the availability of the position-related data at the positioning system

interface; at initialisation of the positioning system, latency is also defined as the TTFF.

Autonomous applications require a high level of integrity in different operating environments,

and security risks to GNSS (jamming, spoofing) are a rising concern. Some military, SoL or

critical applications are required to use encrypted signals or complementary redundant PNT

systems. The concept of these mitigation strategies involves resilient, robust and assured PNT

150

systems. Some of the requirements in terms of security are as follows (3GPP, 2018; GSA,

2019a; SAE International, 2018):

• Robustness: a qualitative, rather than a quantitative, parameter that depends on the type

of attack or interference the receiver is capable of mitigating. It can include

authentication information to assure users that the signal comes from a valid source

(enabling sensitive applications).

• Security: The PNT system shall enable encrypted positioning signals and secure data

links as necessary. UAV/UE security and cybersecurity shall be maintained to reduce

the possibility of, for example, tampering or spoofing.

• Resilience: The PNT system shall provide for an alternative PNT source when the

primary source of PNT (typically GNSS) does not meet the required level of accuracy,

availability, integrity or continuity. Transition to a secondary PNT source(s) upon

losing a primary PNT source shall maintain safety, security, continuity and minimal

PNT service disruptions.

• Authentication: provision of assurance that the position-related data associated with

the UE have been derived from trusted and authorised sources (e.g. real, falsified

signals). This KPI is different from security, which defines measures to ensure that

position-related data are safeguarded against unapproved disclosure or usage inside or

outside the positioning system. Because it cannot be summarised and quantified as a

scalar target, this KPI is managed as a binary field in the present report: a yes-or-no

provision for positioning authentication is needed.

• Privacy: measures to ensure that position-related data are safeguarded against

unapproved disclosure or usage inside or outside the positioning system and/or to

ensure that a non-authorised party cannot access information relating to the privacy of

the user. Because it cannot be summarised and quantified as a scalar target, this KPI is

managed as a binary field in the present report: yes-or-no security and/or privacy is

needed.

For mobile applications where device size and battery life is limited (e.g. smartphones, tablets,

UAV, asset management), power is a primary consideration when designing UE. The power

consumption will vary depending on the available signals and data, and PNT sensors or

operation are modified to meet power requirements. For example, in LBS, the GNSS tracking

in smartphones is turned off to save battery power when in idle mode, this limits the application

151

of techniques like PPP where a long converge time is required. This requirement is defined as

follows (3GPP, 2018):

• Power consumption: electrical power (usually in mW) used by the positioning system

to produce the position-related data.

• Energy per fix: electrical energy (usually in mJ per fix) used by the positioning system

to produce position-related data. It represents the integrated power consumption of the

positioning system over the required processing interval. It considers both the

processing energy and the energy used during the idle state between two successive

productions of position-related data. This KPI can advantageously replace power

consumption when the positioning system is not active continuously (e.g. device

tracking).

In land-based mobile applications, navigation parameters can change drastically depending on

the operating environment (e.g. urban, open sky, indoors, tunnels) and motion dynamics (e.g.

pedestrian, wheeled mobile robot, UAV or vehicle motion). Further, the coverage or range

provided by different PNT systems is a characteristic that needs to be considered to match the

environment of use. Some requirement parameters that define these conditions are as follows

(3GPP, 2018):

• Coverage: the coverage provided by a PNT system is that surface area or space volume

in which the signals are adequate to permit the navigator to determine position to a

specified level of accuracy. Coverage is influenced by system geometry, signal power

levels, receiver sensitivity, atmospheric noise conditions and other factors that affect

signal availability (DoD et al., 2017).

• Environment of use: the physical environment in which the UE operates. It describes

the service area or volume (e.g. building, cell or network coverage; regional or global

coverage) as well as the high-level properties affecting RF propagation and positioning,

such as the nature of the service area (e.g. open [i.e. no obstruction], aerial, suburban,

canyons urban or natural, indoor including tunnels). In case of multiple environments,

the attribute shall also define whether the use case is expected to operate seamlessly in

all these environments.

• UE dynamic: the UE can be either static or moving. In the latter, the attribute shall also

provide some elements about its motion (e.g. maximum speed, trajectory).

An additional requirement to consider, proposed by the automotive and telecommunications

industries, is the need for connectivity. Connectivity has an important effect on positioning: as

152

some applications increase their reliance on connected solutions, the PNT solution has the

advantage that it uses the same delivery channel. The 3GPP (3GPP, 2018) has merged these

concepts into a 5G positioning service area, where both communication and the positioning

services that rely on communication are present:

• Connectivity: this requirement refers to the need for a communication and/or

connectivity link of an application to be able to receive and communicate data to third

parties. Connectivity comprises long-range communication technologies such as

satellite, 3G and LTE as well as short-range technologies such as Bluetooth and NFC

(GSA, 2019a).

• 5G positioning service area: a service area where positioning services would solely

rely on infrastructure and positioning technologies that can be assumed to be present

anywhere where 5G is present (e.g. a country-wide operator-supplied 5G network,

GNSS, position/motion sensors). This includes both indoor and outdoor environments,

and for the latter, any outdoor environment (e.g. rural with a low density of node but

little obstruction, urban with a high density of node and obstruction by buildings). The

5G positioning service area can be considered for use cases that must work in any 5G

environment; for example in any building—commercial, public or residential alike—

or to localise a patient suffering a heart attack in an apartment building.

As PNT services become more widespread in new market segments and different system

providers operate in different segments, two important requirement parameters to consider are

interoperability and traceability. The automotive and telecommunications industry

incorporates these into their specifications. Additionally, the rail transport industry references

the concept of maintainability:

• Maintainability is determined by the ease with which the product or system can be

repaired or maintained.

• Interoperability refers to the ability of different vehicle positioning systems with

different absolute positioning capacities to be used on the road network and still meet

the required relative positioning performance requirements. Meeting an interoperability

requirement requires that all vehicle positioning systems operate with some level of

minimum consistency in terms of positioning algorithms, hardware, signals and

infrastructure (Austroads, 2013).

• Traceability refers to a traceable measurement that can be related to national or

international standards using an unbroken chain of measurements, each of which has a

153

stated uncertainty. For finance applications, knowledge of the traceability of the time

signal to coordinated universal time is essential to ensure regulatory compliance at the

timestamp (GSA, 2019a p. 107).

154

Master's thesis of Science: User expectations of a national, high-accuracy GNSS positioning service for professional applications: an Australian context

User expectations of a national, high-accuracy GNSS positioning service for

professional applications: an Australian context

Luis Elneser Gonzalez

DECLARATION

ACKNOWLEDGEMENTS

TABLE OF CONTENTS

Declaration.............................................................................................................................................. i

Acknowledgements ............................................................................................................................... ii

Table of Contents ................................................................................................................................. iii

List of Figures ...................................................................................................................................... vii

List of Tables ........................................................................................................................................ ix

List of Abbreviations ........................................................................................................................... xi

Abstract .................................................................................................................................................. 1

Chapter 1: Introduction ....................................................................................................................... 2

1

Introduction ................................................................................................................................... 3

Chapter 2: Positioning and Navigation Methods ............................................................................. 10

2

Positioning and Navigation Methods ......................................................................................... 11

Chapter 3: PNT Infrastructure ......................................................................................................... 35

3

PNT Infrastructure ..................................................................................................................... 36

Chapter 4: GNSS Augmentation Services for Professional High-Accuracy Applications .... 51

4 GNSS Augmentation Services for Professional High-Accuracy Applications ................ 52

Chapter 5: PNT Requirements for Professional High-Accuracy Applications ............................. 66

5

PNT Requirements for Professional High-Accuracy Applications ........................................ 67

Chapter 6: Evaluation of PNT Services for Professional High-Accuracy Applications ............. 102

6 Evaluation of PNT Services for Professional High-Accuracy Applications ........................ 103

Chapter 7: Conclusions and Recommendations ............................................................................ 119

7 Conclusions and Recommendations ........................................................................................ 120

References .......................................................................................................................................... 125

Appendix A: Accuracy Measures .................................................................................................... 145

Appendix B: Performance Parameters ........................................................................................... 149

LIST OF FIGURES

LIST OF TABLES

LIST OF ABBREVIATIONS

ABSTRACT

CHAPTER 1: INTRODUCTION

1 INTRODUCTION

1.1 BACKGROUND

1.2 JUSTIFICATION

1.3 RESEARCH QUESTIONS

1.4 PROJECT OUTCOMES

1.5 THESIS OUTLINE

CHAPTER 2: POSITIONING AND NAVIGATION

METHODS

2 POSITIONING AND NAVIGATION METHODS

2.1 OVERVIEW OF POSITIONING AND NAVIGATION

2.2 SATELLITE-BASED POSITIONING AND NAVIGATION

Satellite block

Band Frequency (MHz) Signal*

Access

Service

I/II/IIA/IIR

IIR-M

IIF

III

Satellite block

Band

Signal*

Access

Service

Frequency (MHz)

Multiplexing mode

Band

Access

Service

Frequency (MHz) 1561.098

Signal* B1I

Band Frequency (MHz) Signal* Access

Service OS

Band Frequency (MHz)

Signal

Access

Service

Band Frequency (MHz)

Signal

Access