Sequential covariance intersection fusion Kalman filter for multiple time-delay sensor network systems with colored noises

This paper is concerned with the fusion estimation problem for multi-sensor discrete time-invariant linear systems with multiple time delays and colored measurement noise. In order to transform those systems into systems with correlated white noise, a system transformation method is introduced. A se...

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Bibliographic Details
Published in2017 36th Chinese Control Conference (CCC) pp. 5282 - 5287
Main Authors Jun Wang, Tianmeng Shang, Yuan Gao, Chenjian Ran, Yinlong Huo, Gang Hao, Yun Li
Format Conference Proceeding
LanguageEnglish
Published Technical Committee on Control Theory, CAA 01.07.2017
Subjects
Colored measurement noises
Colored noise
Covariance matrices
Cross covariance
Delay effects
Kalman filters
Multi time delays
Noise measurement
SCI fusion
Time measurement
White noise
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Summary:This paper is concerned with the fusion estimation problem for multi-sensor discrete time-invariant linear systems with multiple time delays and colored measurement noise. In order to transform those systems into systems with correlated white noise, a system transformation method is introduced. A sequential covariance intersection (SCI) fusion Kalman filter is given based on the local optimal recursive Kalman filter in the linear minimum variance sense, which avoids the calculation of the cross covariance matrices between local sensors. It is proved that the presented fused Kalman filter has higher accuracy than those local filters. The simulation result reveals that the actual accuracy of the SCI fusion Kalman filter approximates to distributed fusion Kalman filter weighted by matrices, and based on the covariance ellipse, the geometric interpretation with respect to accuracy relation of the local and the fused Kalman filter is shown.
ISSN:2161-2927
DOI:10.23919/ChiCC.2017.8028191