Hypothesis Exploration in Multiple Hypothesis Tracking with Multiple Clusters

Finding the most probable posterior hypotheses is a core task in hypothesis-oriented multiple hypothesis tracking (HO-MHT), and also in related tracking methods such as the Poisson multi-Bernoulli mixture (PMBM) filter. The traditional approach is to find the M best new hypotheses for each parent hypothesis by means of Murty's algorithm. In this paper we instead present an algorithm for finding the M best hypotheses ranging over all parent hypotheses. The algorithm is developed in the more general context of cluster management, where the goal is to merge several parent clusters, and to find the M best posterior hypotheses in any such supercluster.