Integrated Clutter Estimation and Target Tracking Using JIPDA/MHT Tracker

摘要

In this paper, the problem of tracking multiple targets in unknown clutter background using the Joint Integrated Probabilistic Data Association (JIPDA) tracker and the Multiple Hypotheses Tracker (MHT) is studied. It is common in real tracking problems to have little or no prior information on clutter background. Furthermore, the clutter background may be dynamic and evolve with time. Thus, in order to get accurate tracking results, trackers need to estimate parameters of clutter background in each sampling instant and use the estimate to improve tracking. In this paper, incorporated with the JIPDA tracker or the MHT algorithm, a method based on Non-homogeneous Poisson point processes is proposed to estimate the intensity function of non-homogeneous clutter background. In the proposed method, an approximated Bayesian estimate for the intensity of non-homogeneous clutter is updated iteratively through the Normal-Wishart Mixture Probability Hypothesis Density (PHD) filter technique. Then, the above clutter density estimate is used in the JIPDA algorithm and the MHT algorithm for multitarget tracking. It is demonstrated thorough simulations that the proposed clutter background estimation method improves the performance of the JIPDA tracker in unknown clutter background.