Distributed full-order optimal fusion filters and smoothers for discrete-time stochastic singular systems

Abstract

The optimal fusion problem for the state estimation of discrete-time stochastic singular systems is considered. The key idea is to convert a stochastic singular system with multiple sensors and correlated noises into an equivalent group of non-singular systems. Based on the state estimation for each local non-singular system, the optimal full-order filters and smoothers with a three-layer fusion structure are obtained for the original system using the optimal weighted fusion algorithms in the linear minimum variance sense. A simulation example shows that the fusion estimator is better than each local one.