Conditions for MHT to be an Exact Bayesian Solution to the Multiple Target Tracking Problem

This paper finds conditions under which Multiple Hypothesis Tracking (MHT) is an exact Bayesian solution to the multiple-target tracking problem. The crucial condition is that measurements arrive in scans, but otherwise the conditions are minimally restrictive. In order to produce a computationally feasible implementation of MHT, some approximations must be made, but this true is for any (existing) method of producing an exact Bayesian solution. Limiting the number of hypotheses considered is an example of such an approximation. This paper is motivated by recent claims that MHT is not theoretically rigorous or “Bayes optimal”.