OPTIMAL FUSION REDUCED-ORDER KALMAN ESTIMATORS FOR DISCRETE-TIME STOCHASTIC SINGULAR SYSTEMS

S.L. Sun, Y.L. Shen, and J. Ma

Keywords

Multisensor, optimal information fusion, reducedorder Kalman estimator, crosscovariance, stochastic singular system

Abstract

Based on the optimal fusion algorithm weighted by scalars in the linear minimum variance (LMV) sense, the distributed optimal fusion reduced-order Kalman estimators including predictor, filter and smoother are presented for discrete-time stochastic singular linear systems with multiple sensors and correlated noises. The fusion estimation problem of original high-order singular system is transferred to that of two reduced-order subsystems. They have better precision than any local estimators from every sensor do. The estimation error cross-covariance matrices between any two sensor subsystems are derived for two reduced-order subsystems, respectively. Furthermore, the steady-state fusion estimators are also investigated, which have the reduced online computational burden. A simulation example with three sensors shows the effectiveness.

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