Sequential fusion estimation for multisensor systems with non-Gaussian noises

被引：6
|
作者：
Yan, Liping [1 ,2 ]
Di, Chenying [1 ]
Wu, Q. M. Jonathan [2 ]
Xia, Yuanqing [1 ]
机构：
[1] Beijing Inst Technol, Sch Automat, Key Lab Intelligent Control & Decis Complex Syst, Beijing 100081, Peoples R China
[2] Univ Windsor, Dept Elect & Comp Engn, Windsor, ON N9B 3P4, Canada
来源：
SCIENCE CHINA-INFORMATION SCIENCES | 2020年 / 63卷 / 12期
基金：
北京市自然科学基金;
关键词：
state estimation; sequential fusion; non-Gaussian disturbance; heavy-tailed noise; multivariate t-distribution;
D O I：
10.1007/s11432-019-2725-8
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The sequential fusion estimation for multisensor systems disturbed by non-Gaussian but heavy-tailed noises is studied in this paper. Based on multivariate t-distribution and the approximate t-filter, the sequential fusion algorithm is presented. The performance of the proposed algorithm is analyzed and compared with the t-filter-based centralized batch fusion and the Gaussian Kalman filter-based optimal centralized fusion. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective. As the generalization of the classical Gaussian Kalman filter-based optimal sequential fusion algorithm, the presented algorithm is shown to be superior to the Gaussian Kalman filter-based optimal centralized batch fusion and the optimal sequential fusion in estimation of dynamic systems with non-Gaussian noises.
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页数：13
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