LMMSE estimation of Markovian jump linear systems with random parameters and estimate feedback

This paper considers the state estimation of Markovian jump linear systems with random parameters and estimate feedback. The state estimate at the previous epoch is introduced into the dynamical model to depict some phenomena that the system evolvement may depend on the most recent estimate. Then, the linear minimum mean square error estimator is derived for the considered system. A filtering framework for state estimation and data association for multiple maneuvering targets tracking is presented via the considered system, by using the state estimate at previous epoch to model the false echo which is dropped into the (overlapped) validation regions and the random parameters to describe the uncertainty between targets and possible echoes. A simulation about tracking two closely maneuvering targets in clutter shows the effectiveness of the proposed method.