2007 Volume 50 Issue 167 Pages 18-26
This paper considers the joint detection and filtering problem of discrete-time stochastic systems when the measurements are interrupted in a random fashion. By formulating the measurement interruptions into two-state Markov chains, a sequential multiple model filter is developed from the Bayesian point of view. With a soft switching mechanism, the proposed filter automatically abandons the useless measurements in the interrupted time intervals, and captures the correct measurements for recursive estimation. Compared with the widely used interacting multiple model filter, the new filter has a more simple structure and requires less time for computation. A numerical example shows that the proposed multiple model filter can effectively solve the target tracking problem with interrupted range measurements.