PMHT Algorithms for Multi-Frame Assignment

Abstract

Probabilistic multi-hypothesis tracking (PMHT) is an algorithm for tracking multiple targets when measurement-to-target assignments are unknown and must be estimated jointly with the target tracks. PMHT is linear in the number of targets and the number of measurements; moreover, it is guaranteed to converge to locally optimal state estimates. However, it violates the rule that no target can be assigned more than one measurement. This hereby leads to a plethora of local maxima that cause performance problems. These problems are greatly reduced by applying the PMHT method to multi-frame data sequences, that is, to the set of all possible measurement sequences in the last L scans. The blend of PMHT and limited enumeration reduces the mismatch induced by violating the "at most one measurement per target" rule. Two new PMHT algorithms are presented. Both are linear in the number of targets and the number of enumerated sequences