Comparing Multitarget Multisensor ML-PMHT with ML-PDA for VLO Targets.

摘要

The Maximum Likelihood Probabilistic Data Association (ML-PDA) tracker and the Maximum Likelihood Probabilistic Multi-Hypothesis (ML-PMHT) tracker are tested in their capacity as algorithms for very low observable targets (VLO, meaning 6 dB post-signal-processing or even less) and are then applied to five synthetic benchmark multistatic active sonar scenarios featuring multiple targets, multiple sources and multiple receivers. 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 implemented as a true multitarget tracker. This formulation gives ML-PMHT superior performance for instances where multiple targets are closely spaced with similar motion dynamics.