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Extended kalman filter

Xem 1-20 trên 23 kết quả Extended kalman filter
  • This paper presents an extended Kalman filtering (EKF) algorithm for estimating immeasurable state variables of a vehicle stability control system. Initially, the steering angle and vertical forces on the tires were considered inputs of the estimator.

    pdf11p vioishi2711 01-07-2019 6 0   Download

  • Báo cáo đề xuất cải tiến chất lượng của bộ lọc Kalman mở rộng cho bài toán định vị cho robot di động. Một hệ logic mờ được sử dụng để hiệu chỉnh theo thời gian thực các ma trận hiệp phương sai của bộ lọc. Tiếp đó, một mạng nơron được cài đặt để hiệu chỉnh các hàm thành viên của luật mờ. Mục đích là để tăng độ chính xác và tránh sự phân kỳ của bộ lọc Kalman khi các ma trận hiệp phương sai được chọn cố định hoặc chọn sai.

    pdf13p binhminhmuatrenngondoithonggio 09-06-2017 39 2   Download

  • This filter uses bearings only measurements to estimate the target state in passive target tracking scenario. This work combines the MGEKF and the iteration method. The filter utilizes the updated state to re-linearize the measurement equation. Then the proposed work is tested in a two dimensional scenario. The simulation study compares the IMGEKF and some other filters to show the improvement.

    pdf5p praishy2 27-02-2019 6 0   Download

  • The Extended Kalman Filter (EKF) provides an efficient method for generating approximate maximum-likelihood estimates of the state of a discrete-time nonlinear dynamical system (see Chapter 1). The filter involves a recursive procedure to optimally combine noisy observations with predictions from the known dynamic model. A second use of the EKF involves estimating the parameters of a model (e.g., neural network) given clean training data of input and output data (see Chapter 2).

    pdf51p duongph05 07-06-2010 122 15   Download

  • This paper addresses the problem of blind separation of non stationary signals. We introduce an online separating algorithm for estimation of independent source signals using the assumption of non-stationarity of sources. As a separating model, we apply a self-organizing neural network with lateral connections, and define a contrast function based on correlation of the network outputs. A separating algorithm for adaptation of the network weights is derived using the state-space model of the network dynamics, and the extended Kalman filter.

    pdf17p vinguyentuongdanh 20-12-2018 10 0   Download

  • This article has presented an adaptive fuzzy controller for permanent magnet synchronous motor. The rotor speed estimation based on reduced-order extended kalman filter (reduced-order EKF). The sensor less algorithm controls have implemented by very high speed integrated circuit hardware description language (VHDL). The simulation work is performed by MATLAB/Simulink and ModelSim co-simulation mode. The simulation results shown that the motor’s speed has good dynamic performance and isn’t sensitive to the parameter variations.

    pdf5p cathydoll5 27-02-2019 15 0   Download

  • This paper proposes a new state-space model that can well represent a real threephase power system by taking into account the unbalance conditions and harmonic distortion of a three-phase power system. The model associated with Extended Kalman Filter is then applied to estimate the positive and negative sequences of the fundamental component.

    pdf6p vicapital2711 02-08-2019 5 0   Download

  • Hội thảo toàn quốc về Điện tử - Truyền thông – An toàn thông tin, ATC/REV-2012 Multi-sensor mobile robot and the sensor fusion-based localization with Extended Kalman Filter Trần Thuận Hoàng, Phùng Mạnh Dương, Đặng Anh Việt và Trần Quang Vinh Trường Đại học Công nghệ, Đại học Quốc gia Hà nội e-Mail: thuanhoang@donga.edu.vn Tóm tắt Báo cáo trình bày việc xây dựng khối cảm nhận của một robot di động đa cảm biến và việc dùng phương pháp tổng hợp cảm biến với bộ lọc Kalman mở rộng để định vị chính xác cho robot. Các cảm biến hiện đại như cảm...

    pdf6p thuanhoang70 07-01-2013 265 64   Download

  • NONLINEAR OBSERVATION SCHEME AND DYNAMIC MODEL (EXTENDED KALMAN FILTER) 16.1 INTRODUCTION In this section we extend the results for the linear time-invariant and timevariant cases to where the observations are nonlinearly related to the state vector and/or the target dynamics model is a nonlinear relationship [5, pp. 105– 111, 166–171, 298–300]. The approachs involve the use of linearization procedures. This linearization allows us to apply the linear least-squares and minimum-variance theory results obtained so far.

    pdf10p khinhkha 30-07-2010 95 12   Download

  • DUAL EXTENDED KALMAN FILTER METHODS Eric A. Wan and Alex T. Nelson Department of Electrical and Computer Engineering, Oregon Graduate Institute of Science and Technology, Beaverton, Oregon, U.S.A. 5.1 INTRODUCTION The Extended Kalman Filter (EKF) provides an efficient method for generating approximate maximum-likelihood estimates of the state of a discrete-time nonlinear dynamical system (see Chapter 1). The filter involves a recursive procedure to optimally combine noisy observations with predictions from the known dynamic model.

    pdf51p khinhkha 29-07-2010 85 8   Download

  • In Chapter 2, Puskorius and Feldkamp described a procedure for the supervised training of a recurrent multilayer perceptron – the nodedecoupled extended Kalman filter (NDEKF) algorithm. We now use this model to deal with high-dimensional signals: moving visual images. Many complexities arise in visual processing that are not present in onedimensional prediction problems: the scene may be cluttered with backKalman Filtering and Neural Network

    pdf13p duongph05 07-06-2010 66 14   Download

  • In this chapter, we consider another application of the extended Kalman filter recurrent multilayer perceptron (EKF-RMLP) scheme: the modeling of a chaotic time series or one that could be potentially chaotic. The generation of a chaotic process is governed by a coupled set of nonlinear differential or difference equations.

    pdf40p duongph05 07-06-2010 61 13   Download

  • In this book, the extended Kalman filter (EKF) has been used as the standard technique for performing recursive nonlinear estimation. The EKF algorithm, however, provides only an approximation to optimal nonlinear estimation. In this chapter, we point out the underlying assumptions and flaws in the EKF, and present an alternative filter with performance superior to that of the EKF. This algorithm, referred to as the unscented Kalman filter (UKF), was first proposed by Julier et al. [1–3], and further developed by Wan and van der Merwe [4–7]....

    pdf60p duongph05 07-06-2010 67 13   Download

  • THE UNSCENTED KALMAN FILTER Eric A. Wan and Rudolph van der Merwe Department of Electrical and Computer Engineering, Oregon Graduate Institute of Science and Technology, Beaverton, Oregon, U.S.A. 7.1 INTRODUCTION In this book, the extended Kalman filter (EKF) has been used as the standard technique for performing recursive nonlinear estimation. The EKF algorithm, however, provides only an approximation to optimal nonlinear estimation. In this chapter, we point out the underlying assumptions and flaws in the EKF, and present an alternative filter with performance superior to that of the EKF.

    pdf60p khinhkha 29-07-2010 74 11   Download

  • CHAOTIC DYNAMICS Gaurav S. Patel Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada Simon Haykin Communications Research Laboratory, McMaster University, Hamilton, Ontario, Canada (haykin@mcmaster.ca) 4.1 INTRODUCTION In this chapter, we consider another application of the extended Kalman filter recurrent multilayer perceptron (EKF-RMLP) scheme: the modeling of a chaotic time series or one that could be potentially chaotic. The generation of a chaotic process is governed by a coupled set of nonlinear differential or difference equations.

    pdf40p khinhkha 29-07-2010 68 10   Download

  • LEARNING SHAPE AND MOTION FROM IMAGE SEQUENCES Gaurav S. Patel Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada Sue Becker and Ron Racine Department of Psychology, McMaster University, Hamilton, Ontario, Canada (beckers@mcmaster.ca) 3.1 INTRODUCTION In Chapter 2, Puskorius and Feldkamp described a procedure for the supervised training of a recurrent multilayer perceptron – the nodedecoupled extended Kalman filter (NDEKF) algorithm. We now use this model to deal with high-dimensional signals: moving visual images.

    pdf13p khinhkha 29-07-2010 92 7   Download

  • The discussion about the manned spacecraft program was initiated at NASA in 1959. Only one year later, Dr. Kalman and Dr. Schmidt linked the linear Kalman filter and the perturbation theory in order to obtain the Kalman-Schmidt filter, currently known as the extended Kalman filter. This approach would be implemented in 1961 using an IBM 704 computer (running at approximately 4000 operations per second) for simulation purposes, and subsequently, in July 1969, for making the descent of the Apollo 11 lunar module to the Moon possible....

    pdf0p bi_bi1 11-07-2012 47 5   Download

  • LINEAR TIME-VARIANT SYSTEM 15.1 INTRODUCTION In this chapter we extend the results of Chapters 4 and 8 to systems having time-variant dynamic models and observation schemes [5, pp. 99–104]. For a time-varying observation system, the observation matrix M of (4.1-1) and (4.1-5) could be different at different times, that is, for different n. Thus the observation equation becomes Y n ¼ M nX n þ N n ð15:1-1Þ For a time-varying dynamics model the transition matrix È would be different at different times. In this case È of (8.1-7) is replaced by Èðt n ; t nÀ1 Þ to indicate a dependence...

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  • Introduction Least-Squares Estimation Properties of Estimators Best Linear Unbiased Estimation Maximum-Likelihood Estimation Mean-Squared Estimation of Random Parameters Maximum A Posteriori Estimation of Random Parameters The Basic State-Variable Model State Estimation for the Basic State-Variable Model Prediction • Filtering (the Kalman Filter) • Smoothing Jerry M. Mendel University of Southern California 15.10 Digital Wiener Filtering 15.11 Linear Prediction in DSP, and Kalman Filtering 15.12 Iterated Least Squares 15.

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  • EURASIP Journal on Applied Signal Processing 2003:13, 1268–1278 c 2003 Hindawi Publishing Corporation Extended Kalman Filter Channel Estimation for Line-of-Sight Detection in WCDMA Mobile Positioning Abdelmonaem Lakhzouri Institute of Communications Engineering, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland Email: abdelmonaem.lakhzouri@tut.fi Elena Simona Lohan Institute of Communications Engineering, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland Email: elena-simona.lohan@tut.

    pdf11p sting12 10-03-2012 27 3   Download

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