MHT and ITS: A Look at Single Target Tracking in Clutter

Abstract:
Multiple hypothesis testing (MHT) is the targets tracking algorithm, where target states are estimated from each data association hypothesis. As more measurements are received, the probabilities of local hypotheses are calculated recursively using all available information such as density of unknown targets, density of false targets, probability of detection and location uncertainty. The success of MHT depends on the ability to maintain a small list of potential hypotheses, which can be facilitated with the accurate object detectors. At this time, the integrated track splitting (ITS) filter is multiscan target tracking algorithm, which is capable to integrate a number of tracks with its probability of target existence. In this paper, We present a number of experiments with different clutter density, in order to determine accuracy of both algorithms. The results are preliminary in terms of single target tracking comparative analysis.