Convergence analysis and improvement of progressive Gaussian approximation filters

被引：5
作者：
He, Wenxiu [1 ]
Yang, Xusheng [2 ]
Qiu, Xiang [2 ]
机构：
[1] Zhejiang Univ Technol, Zhijiang Coll, Shaoxing, Peoples R China
[2] Zhejiang Univ Technol, Coll Informat & Engn, Hangzhou, Peoples R China
来源：
SIGNAL PROCESSING | 2022年 / 200卷
基金：
中国国家自然科学基金;
关键词：
Nonlinear filtering; Nonlinear Kalman filtering; Convergence analysis; CUBATURE KALMAN FILTER; PERFORMANCE EVALUATION; UPDATE;
D O I：
10.1016/j.sigpro.2022.108643
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper studies the convergence problem of progressive Gaussian approximation filters (PGAFs). The convergence analysis of the PGAF is presented based on the Lyapunov method. It is proved that the estimation error is bounded when the convergence conditions hold. Moreover, based on the convergence analysis, the filtering performance of the PGAF is improved by adjusting the strategy of the progressive measurement update. Finally, a simulation experiment of mobile target tracking is designed to verify the effectiveness of the analysis results by comparing the improved PGAF with some existing methods. (c) 2022 Elsevier B.V. All rights reserved.
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页数：8
相关论文
共 24 条
[1]   A Novel Progressive Gaussian Approximate Filter for Tightly Coupled GNSS/INS Integration [J].
Bai, Mingming ;
Huang, Yulong ;
Zhang, Yonggang ;
Jia, Guangle .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2020, 69 (06) :3493-3505
[2]   THE ITERATED KALMAN FILTER UPDATE AS A GAUSS-NEWTON METHOD [J].
BELL, BM ;
CATHEY, FW .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1993, 38 (02) :294-297
[3]   A strong tracking extended Kalman observer for nonlinear discrete-time systems [J].
Boutayeb, M ;
Aubry, D .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1999, 44 (08) :1550-1556
[4]   On Sigma-Point Update of Cubature Kalman Filter for GNSS/INS Under GNSS-Challenged Environment [J].
Cui, Bingbo ;
Wei, Xinhua ;
Chen, Xiyuan ;
Li, Jinyang ;
Li, Lin .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2019, 68 (09) :8671-8682
[5]   Performance analysis of improved iterated cubature Kalman filter and its application to GNSS/INS [J].
Cui, Bingbo ;
Chen, Xiyuan ;
Xu, Yuan ;
Huang, Haoqian ;
Liu, Xiao .
ISA TRANSACTIONS, 2017, 66 :460-468
[6]   Progressive bayes: A new framework for nonlinear state estimation [J].
Hanebeck, UD ;
Briechle, K ;
Rauh, A .
MULTISENSOR, MULTISOURCE INFORMATION FUSION: ARCHITECTURES, ALGORITHMS, AND APPLICATIONS 2003, 2003, 5099 :256-267
[7]  
Hanebeck UD, 2013, 2013 16TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), P1103
[8]  
Huang YL, 2015, IEEE DECIS CONTR P, P4344, DOI 10.1109/CDC.2015.7402897
[9]   Generalized bias compensated pseudolinear Kalman filter for colored noisy bearings-only measurements [J].
Kaba, Utku ;
Temeltas, Hakan .
SIGNAL PROCESSING, 2022, 190
[10]   General Formulation of Kalman-Filter-Based Online Parameter Identification Methods for VSI-Fed PMSM [J].
Li, Xinyue ;
Kennel, Ralph .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2021, 68 (04) :2856-2864
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