Relationship Between Finite Set Statistics and the Multiple Hypothesis Tracker

The multiple hypothesis tracker (MHT) and finite set statistics (FISST) are two approaches to multitarget tracking, which both have been heralded as optimal. In this paper, we show that the multitarget Bayes filter with basis in FISST can be expressed in terms the MHT formalism, consisting of association hypotheses with corresponding probabilities and hypothesis-conditional densities of the targets. Furthermore, we show that the resulting MHT-like method under appropriate assumptions (Poisson clutter and birth models, no target death, linear-Gaussian Markov target kinematics) only differs from Reid's MHT with regard to the birth process.