MINISTRY OF EDUCATION AND TRAINING MINISTRY OF SCIENCE AND TECHNOLOGY
NATIONAL CENTER FOR TECHNOLOGICAL PROGRESS
VU TRUNG KIEN
RESEARCH AND DEVELOPMENT FOR WI-FI BASED
INDOOR POSITIONING TECHNIQUE
SUMMARY OF DOCTORAL THESIS
Field of study: Electronics Engineering
Code: 9520203
HA NOI - 2019
The thesis is completed at:
National Center for Technological Progress
Supervisor: Prof., Dr. Le Hung Lan
Reviewer 1: Assoc. Prof., Dr. Thai Quang Vinh
Reviewer 2: Assoc. Prof., Dr. Ha Hai Nam
Reviewer 3: Assoc. Prof., Dr. Hoang Van Phuc
The thesis shall be defended in front of the Thesis Committee at
Academy Level at National Center for Technological Progress
At...... hour....... date...... month...... year 2019
The thesis can be found at: The Library of National Center for
Technological Progress; The National Library
LIST OF WORKS RELATED TO THE THESIS
HAS BEEN PUBLISHED
[CT1] Hoang Manh Kha, Duong Thi Hang, Vu Trung Kien, Trinh Anh
Vu (2017), Enhancing WiFi based Indoor Positioning by
Modeling measurement Data with GMM, IEEE International
Conference on Advanced Technologies for Communications,
IEEE, Quy Nhon, Vietnam, pp. 325-328
[CT2] Vu, T.K., Hoang, M.K., and Le, H.L. (2018), "WLAN
Fingerprinting based Indoor Positioning in the Precence of
Dropped Mixture Data", Journal of Military Science and
Technology. 57A(3), pp. 25-34.
https://drive.google.com/file/d/1jv2U3tmJq1vUEez6nt6Cq8DzJW
EWZu6-/view
[CT3] Vu, Trung Kien and Le, Hung Lan (2018), "Gaussian Mixture
Modeling for Wi-Fi Fingerprinting based Indoor Positioning in
the Presence of Censored Data", Vietnam Journal of Science,
Technology and Engineering. 61(1), pp. 3-8,
DOI: https://doi.org/10.31276/VJSTE.61(1).03-08
[CT4](ISI-Q2) Vu, Trung Kien, Hoang, Manh Kha, and Le, Hung Lan
(2019), "An EM algorithm for GMM parameter estimation in the
presence of censored and dropped data with potential application
for indoor positioning", ICT Express, 5(2), pp. 120-123,
DOI: 10.1016/j.icte.2018.08.001
Accepted paper:
[CT5](ISI-Q3) Vu, Trung Kien, Hoang, Manh Kha, and Le, Hung Lan
(2019), “Performance Enhancement of Wi-Fi Fingerprinting
based IPS by Accurate Parameter Estimation of Censored and
Dropped Data”, Radioengineering, ISSN: 1805-9600.
Submission: 06/04/2019, Reviews Opened: 27/05/2019,
Accepted: 03/09/2019.
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INTRODUCTION
1. The necessity of the thesis
Satellite based positioning systems such as the GPS (Global
Positioning System) can accurately locate objects in outdoor
environments. However, in indoor environments, because satellite
signals are not transmitted directly to the positioning device, the
accuracy of these systems is greatly reduced. On the other hand, there
are more and more indoor navigation needs, such as positioning for
smartphone users to move in terminals, airports, and commercial
centers; locating for goods in stock; positioning for cars in the parking
lots ... . For these reasons, in recent years, the IPS (Indoor Positioning
System) is interested in research and development.
Among the current indoor positioning technologies, Wi-Fi based
positioning technology in the WLAN (Wireless Local Area Network) is
most commonly used due to some reasons such as: Wi-Fi is available at
most areas, popular mobile devices such as phones and computers are
equipped with Wi-Fi signal transceivers.
According to the above reasons, the author has chosen the topic:
"Research and development for Wi-Fi based indoor positioning
techniques", which delves into the research of RSSIF-IPT (Received
Signal Strength Indication Fingerprinting based Indoor Positioning
Technique).
2. Scope of the study
Researching techniques for positioning the static objects in 2-
dimensional space in indoor environments. Positioning technique
focused on research is RSSIF-IPT. The studied issues include:
Characteristics of Wi-Fi RSSI; modelling the distribution of Wi-Fi
RSSI; algorithm to estimate parameters, optimize the parameters of the
model used to model the distribution of Wi-Fi RSSI; online positioning
algorithm.
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3. Research objectives of the topic
Researching and developing the Wi-Fi RSSI fingerprinting based
indoor positioning technique in order to minimize positioning errors and
optimize positioning time. The detailed research objectives are as
follow:
+ Developing algorithms to estimate the parameters and number of
Gaussian components in GMM (Gaussian Mixture Model) in the
presence of unobservable data;
+ Developing a positioning algorithm for minimizing positioning
errors and optimizing positioning time;
4. Methods
Statistical method for conducting the characteristics of collected data
(Wi-Fi RSSI); analytical method for developing parameter estimation
algorithms and positioning algorithms; Monte Carlo method for
evaluating proposed algorithms; empirical methods on both simulation
data and real data to verify the effectiveness of the proposals applied to
IPS.
5. New findings of the doctoral dissertation
- The parameter estimation algorithm for GMM in the presence of
censored and dropped mixture data [CT2-CT4];
- The model selection algorithm for GMM from incomplete data
[CT5];
- The positioning procedure in the presence of unobservable data
[CT5].
6. Organization of dissertation
The thesis will be divided into 4 chapters: Chapter 1: Overview of Wi-
Fi based IPS. Chapter 2: GMM parameter estimation in the presence of
censored and dropped data. Chapter 3: GMM model selection in the
presence of censored and dropped data. Chapter 4: Positioning algorithm
and experimental results.