State Estimation of Discrete-Time Markov Jump Linear Systems in the Environment of Arbitrarily Correlated Gaussian Noises

【摘要】：This paper is concerned with the state estimation problem of discrete-time Markov jump linear systems where the noises infiuencing the systems are assumed to be arbitrarily correlated Gaussian noises.As a result,two algorithms are proposed.The first algorithm is an optimal algorithm of state estimate in the sense of minimum mean-square error estimate,which can exactly compute the minimum mean-square error estimate of systems state given an observation sequence.The second algorithm is a suboptimal algorithm which is proposed to reduce the computation and storage load of the proposed optimal algorithm.A numerical example is given to evaluate the performance of the proposed suboptimal algorithm.