Sequential Inverse Covariance Intersection Fusion Estimation for Multi-sensor Systems with Multiple Delays and Correlated Noises

In order to deal with the fusion estimation problem for the multi-sensor system with multiple time delays and correlated noises, the state space model is converted into a new system with uncorrelated white noises first. Applying the optimal Kalman filtering method, based on the Inverse Covariance In...

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Bibliographic Details
Published in2019 Chinese Control Conference (CCC) pp. 3462 - 3467
Main Authors Shang, Tianmeng, Liu, Qi, Yu, Kai, Chen, Lizi, Gao, Yuan, Huo, Yinlong, Ran, Chenjian
Format Conference Proceeding
LanguageEnglish
Published Technical Committee on Control Theory, Chinese Association of Automation 01.07.2019
Subjects
conservativeness
correlated noise
Covariance matrices
Delay effects
Delays
Estimation
Kalman filters
multiple time delays
optimal Kalman filter
Sequential Inverse Covariance Intersection
Steady-state
White noise
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Summary:In order to deal with the fusion estimation problem for the multi-sensor system with multiple time delays and correlated noises, the state space model is converted into a new system with uncorrelated white noises first. Applying the optimal Kalman filtering method, based on the Inverse Covariance Intersection (ICI) method, the Sequential Inverse Covariance Intersection (SICI) fusion Kalman Estimators are presented, which avoids the computation of the cross-covariances. Compared with the traditional Covariance Intersection (CI) algorithm, the ICI algorithm has less conservativeness, and the SICI structure can handle a wider class of fusion problems. The accuracy relations among the local estimators and the fused estimators are proved. The simulation example shows the effectiveness and the consistence of the proposed SICI fusers, and gives the geometric interpretation of the accuracy relations.
ISSN:2161-2927
DOI:10.23919/ChiCC.2019.8866281