A novel suboptimal algorithm for state estimation of Markov jump linear systems

Abstract

This paper is concerned with state estimation problem for Markov jump linear systems where the disturbances involved in the systems equations and measurement equations are assumed to be Gaussian noise sequences. Based on two properties of conditional expectation, orthogonal projective theorem is applied to the state estimation problem of the considered systems so that a novel suboptimal algorithm is obtained. The novelty of the algorithm lies in using orthogonal projective theorem instead of Kalman filters to estimate the state. A numerical comparison of the algorithm with the interacting multiple model algorithm is given to illustrate the effectiveness of the proposed algorithm.