This paper proposes a maneuvering target tracking algorithm using multiple model filters. This filtering algorithm is discussed in terms of tracking performance, tracking success rate and tracking accuracies for short sampling interval as compared with other conventional methodology. Through several simulations, validity of this algorithm has been confirmed.

This paper presents a new multi-target data association method for automotive radar which we call the order statistics joint probabilistic data association (OSJPDA). The method is formulated using the association probabilities of the joint probabilistic data association (JPDA) filter and an optimal target-to-measurement data association is accomplished using the decision logic algorithm. Simulation results for heavily cluttered conditions show that the tracking performance of the OSJPDA filter is better than that of the JPDA filter in terms of tracking accuracy by about 18%.