Self-tuning weighted measurement fusion Wiener filter and its convergence

Authors:
Yuan Gao;Weiling Wang;Zili Deng
Affiliations:
Department of Automation, Heilongjiang University, Harbin, China;Department of Automation, Heilongjiang University, Harbin, China;Department of Automation, Heilongjiang University, Harbin, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Year:
2009

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Abstract

For the multisensor system with identical measurement matrix and correlated measurement noises, by correlated method, the online estimators of the noise statistics are obtained. Based on modern time series analysis method, a self-tuning weighted measurement fusion Wiener filter is presented, which avoids Lyapunov and Riccati equations, reduces the computational burden and is suitable for real time application. By dynamic error system analysis (DESA) method, it is rigorously proved that the proposed self-tuning Wiener filter converges to the optimal Wiener filter in a realization or with probability one, i.e. it has asymptotical global optimality. A simulation example for a target tracking systems with 3 sensors shows its effectiveness.