A Critical Look at the PMHT
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We combine concepts from numerous papers to provide a derivation and description of a generalized Probabilistic Multi-Hypothesis Tracker that can track multiple targets in a cluttered environment, utilizing multiple sensors and feature measurements, if available. Additionally, we provide a full derivation of the algorithm, including parts omitted or abbreviated in other work. We also provide an improved analytic solution for the prior target-measurement prob-abilities conditioned on the number of observations, a simplified method of performing the maximization step of the algorithm when multiple sensors are used, a consistent covariance approximation of the algorithm when using multiple sensors, explore the use of deterministic annealing to improve performance, and discuss implementation difficulties.
