Weighted Fusion Robust Steady‐State Kalman Filters for Multisensor System with Uncertain Noise Variances

Wen-Juan Qi, Peng Zhang and Zi-Li Deng

Journal of Applied Mathematics, 2014, vol. 2014, issue 1

Abstract: A direct approach of designing weighted fusion robust steady‐state Kalman filters with uncertain noise variances is presented. Based on the steady‐state Kalman filtering theory, using the minimax robust estimation principle and the unbiased linear minimum variance (ULMV) optimal estimation rule, the six robust weighted fusion steady‐state Kalman filters are designed based on the worst‐case conservative system with the conservative upper bounds of noise variances. The actual filtering error variances of each fuser are guaranteed to have a minimal upper bound for all admissible uncertainties of noise variances. A Lyapunov equation method for robustness analysis is proposed. Their robust accuracy relations are proved. A simulation example verifies their robustness and accuracy relations.

Date: 2014
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https://doi.org/10.1155/2014/369252

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2014:y:2014:i:1:n:369252

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