Optimal estimation for continuous-time Markovian jump linear systems with delayed measurements

This paper is to investigate the linear minimum mean square error estimation for continuous-time Markovian jump linear systems with delayed measurements. The key technique applied for treating the measurement delay is the reorganization innovation analysis, by which the state estimation with delayed measurements is transformed into a standard linear mean square filter of an associated delay-free system. The optimal filter is derived based on the innovation analysis method together with geometric arguments in Hilbert space. An analytical solution to the filter is obtained in terms of two Riccati differential equations, and hence is very simple in computation. Computer simulations are carried out to evaluate the performance of the proposed algorithms. The problem of tracking a maneuvering target is addressed.