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, 1-11

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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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnljam:369252

DOI: 10.1155/2014/369252

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