ML-PDA and ML-PMHT: Comparing Multistatic Sonar Trackers for VLO Targets Using a New Multitarget Implementation

The Maximum Likelihood Probabilistic Data Association (ML-PDA) tracker and the Maximum Likelihood Probabilistic Multi-Hypothesis (ML-PMHT) tracker are applied to five synthetic benchmark multistatic active sonar scenarios featuring multiple targets, multiple sources and multiple receivers. For each of the scenarios, Monte Carlo testing is performed to quantify the performance differences between the two algorithms. Both methods end up performing well in situations where there is a single target or widely-spaced targets. However, ML-PMHT has an inherent advantage over ML-PDA in that its likelihood ratio has a simple multitarget formulation, which allows it to be implemeted as a true multitarget tracker. This formulation, presented here for the first time, gives ML-PMHT superior performance for instances where multiple targets are closely spaced with similar motion dynamics.