Robust weighted measurement fusion Kalman filter for uncertain multi-sensor descriptor system with colored measurement noises

被引：0

作者：

Tao, Guili ^{[1
]}

Wang, Xuemei ^{[1
]}

Liu, Wenqiang ^{[2
]}

Zhang, Jianfei ^{[3
]}

Yu, Haiying ^{[3
]}

机构：

[1] Commun Univ Zhejiang, Coll Media Engn, Hangzhou 310018, Peoples R China

[2] Zhejiang Gongshang Univ, Hangzhou 310018, Peoples R China

[3] Heilongjiang Univ Sci & Technol, Harbin 150022, Peoples R China

来源：

2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC) | 2021年

基金：

中国国家自然科学基金;

关键词：

Multi-sensor descriptor system; weighted measurement fusion; uncertain noise variances; colored noises; robust Kalman filter; ESTIMATORS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

For uncertain multi-sensor descriptor stochastic linear system with colored measurement noises, by applying the singular value decomposition, the original system can be transformed into two reduced-order subsystems with correlated noises by means of difference transformation of the measurement equation. Then the problem of weighted measurement fusion Kalman filtering is transformed into prediction problem for subsystems. The robust weighted measurement fusion time-varying Kalman predictor is presented for the first subsystem. Based on the relationship between subsystems and original system, the robust weighted measurement fusion Kalman filter of the original multi-sensor descriptor system is presented. The effectiveness of the proposed algorithm is verified by a numerical simulation example.

引用

页码：3275 / 3282

页数：8

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