Linear minimum-mean-square error estimation of Markovian jump linear systems with stochastic coefficient matrices

This study presents the state estimation problem of discrete-time Markovian jump linear systems with stochastic coefficient matrices which is motivated by the idea of establishing the general filter framework of the joint state estimation and data association in clutters for tracking the manoeuvering target. According to the orthogonality principle, the linear minimum-mean-square error estimator for this system (abbreviated as LMSCE estimator) is derived recursively and sufficient conditions are given for the stability of the LMSCE estimator. The simulation about tracking the manoeuvering target in clutters shows that the LMSCE estimator obtains much more accurate estimate than the well-known interacting multiple model probabilistic data association filter.