A comparison of the JPDAF and PMHT tracking algorithms

Abstract

Here we analyze the tracking characteristics of a new data-association/tracking algorithm proposed by Streit and Luginbuhl, the probabilistic multi-hypothesis tracker (PMHT). The algorithm uses a recursive method (known amongst statisticians as the expectation-maximization or EM method) to compute in an optimal way the associations between the measurements and targets. Until now, no comparative performance analysis has been done. We compare the performance of this new scheme to that of a commonly used tracking algorithm, the joint probabilistic data association filter (JPDAF).