The ML-PMHT Multistatic Tracker for Sharply Maneuvering Targets

The maximum likelihood probabilistic multi-hypothesis tracker (ML-PMHT) is applied to a benchmark multistatic active sonar scenario with multiple targets, multiple sources, and multiple receivers. We first compare the performance of the tracker on this scenario when it is applied in Cartesian measurement space, a typical implementation for many trackers, against its performance in delay-bearing measurement space, where the measurement uncertainty is more accurately represented. ML-PMHT is a batch tracker, and the motion of a target being tracked must be given a parameterization that describes the motion of the target throughout the batch. In the scenario in which we apply the tracker, the majority of target returns have low amplitudes (i.e., the targets are low-observable), which makes the choice of a batch tracker very appropriate. In prior work, ML-PMHT was implemented with a straight-line parameterization to describe target motion. However, in order to track maneuvering targets, the tracker was implemented in a sliding-batch fashion under the assumption that a maneuvering track could be approximated as a series of short straight lines. Here, we augment the straight-line parameterization by a maneuver-a single course change within the batch-that allows ML-PMHT to follow even sharply maneuvering targets, and we apply it in both Cartesian and delay-bearing measurement space. We also implement this maneuvering-model parameterization with both a fixed batch-length implementation as well as a variable batch-length implementation. Finally, we develop an expression for the Cramer-Rao lower bound (CRLB) for the maneuvering-model parameterization and show that the ML-PMHT tracker with the maneuvering-model parameterization is an efficient estimator.