Sequential fusion estimation for multisensor systems with non-Gaussian noises

被引：0

作者：

Liping YAN ^{[1
,2
]}

Chenying DI ^{[1
]}

Q.M.Jonathan WU ^{[2
]}

Yuanqing XIA ^{[1
]}

机构：

[1] Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation,Beijing Institute of Technology

[2] Department of Electrical and Computer Engineering, University of Windsor

来源：

ScienceChina(InformationSciences) | 2020年 / 63卷 / 12期

基金：

北京市自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TP212.9 [传感器的应用];

学科分类号：

摘要：

The sequential fusion estimation for multisensor systems disturbed by non-Gaussian but heavytailed 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.

引用

页码：153 / 165

页数：13

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