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2014 Weighted Fusion Robust Steady-State Kalman Filters for Multisensor System with Uncertain Noise Variances
Wen-Juan Qi, Peng Zhang, Zi-Li Deng
J. Appl. Math. 2014: 1-11 (2014). DOI: 10.1155/2014/369252

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.

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Wen-Juan Qi. Peng Zhang. Zi-Li Deng. "Weighted Fusion Robust Steady-State Kalman Filters for Multisensor System with Uncertain Noise Variances." J. Appl. Math. 2014 1 - 11, 2014. https://doi.org/10.1155/2014/369252

Information

Published: 2014
First available in Project Euclid: 2 March 2015

zbMATH: 1355.93201
MathSciNet: MR3233764
Digital Object Identifier: 10.1155/2014/369252

Rights: Copyright © 2014 Hindawi

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