Performance analysis of Adaptive Probabilistic Multi-hypothesis Tracking with the Metron data sets

The Probabilistic Multi-hypothesis Tracking (PMHT) algorithm is a batch type multi-target tracking algorithm based on the Expectation-Maximization (EM) method. Unlike other popular batch methods (e.g., Multi-Hypothesis Tracking, MHT) the computational burden of PMHT grows linearly in the size of the batch, the number of clutter detections, and the number of targets tracked. this is achieved by employing the independent assignment model for assigning measurements to tracks which gives rise to a different likelihood function that that used by the other methods. In practice, however, the PMHT often exhibits slow convergence to a non-global local peak of the relevant likelihood function. The authors have modified the E-M based optimization method and significantly improved the convergence behavior. This study investigates the ability of Adaptive PMHT to hold track on contacts in a field of active receivers. Metron Inc. has constructed a collection of simulated multi-static active sonar data sets designed to approximate the performance of a buoy field. Each scenario contains multiple maneuvering targets that exhibit frequent dropouts and aspect dependent SNR and these situations are of particular interest.