Inverse Covariance Intersection Fusion Steady-state Kalman Filter for Uncertain Systems with Missing Measurements and Linearly Correlated White Noises

被引：0

作者：

Wang, Xuemei ^{[1
]}

Tao, Guili ^{[1
]}

Zou, Xuelan ^{[1
]}

Hong, Liang ^{[1
]}

机构：

[1] Commun Univ Zhejiang, Coll Media Engn, Hangzhou 310018, 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：

fictitious noise; covariance intersection (CI); inverse covariance intersection (ICI); Kalman filter;

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 systems with missing measurements and linearly correlated white noises, by introducing the fictitious noises to compensate the multiplicative noise terms, the original system can be converted into one with only noise variances. The steady-state Kalman filter is designed by inverse covariance intersection (ICI) fusion. It overcomes the disadvantage that the covariance intersection (CI) fuser has larger conservativeness. The accuracy of the ICI filter is higher than CI filter and that of local filter. A simulation example is given to verify the accuracy relations.

引用

页码：3209 / 3213

页数：5

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