Journal of Applied Mathematics

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

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.

Article information

Source
J. Appl. Math., Volume 2014 (2014), Article ID 369252, 11 pages.

Dates
First available in Project Euclid: 2 March 2015

https://projecteuclid.org/euclid.jam/1425305725

Digital Object Identifier
doi:10.1155/2014/369252

Mathematical Reviews number (MathSciNet)
MR3233764

Zentralblatt MATH identifier
1355.93201

Citation

Qi, Wen-Juan; Zhang, Peng; Deng, Zi-Li. Weighted Fusion Robust Steady-State Kalman Filters for Multisensor System with Uncertain Noise Variances. J. Appl. Math. 2014 (2014), Article ID 369252, 11 pages. doi:10.1155/2014/369252. https://projecteuclid.org/euclid.jam/1425305725

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