Multi-target tracking in underwater multistatic AUV networks with a robust Poisson Multi-Bernoulli filter

Abstract

The study investigates underwater multi-target tracking (MTT) in a multistatic system using autonomous underwater vehicles (AUVs) as receivers separately from transmitters. The most critical aspect of MTT is the accurate extraction of target tracks from the collected measurements, which can be affected by clutter, missed detections, and port-starboard ambiguities. Moreover, the detection parameters—the target-detection probability and clutter rate—used in MTT methods are typically unknown and change with varying environmental conditions and geometric distributions of the AUV, target, and sound transmitter. In view of these challenges, we propose a robust multi-sensor Poisson Multi-Bernoulli Mixture (R-MS-PMBM) filter immune to unknown detection parameters that treats clutter rates and detection probabilities as continuous random variables modeled by beta and gamma distributions, respectively. The R-MS-PMBM filter is a conjugate prior, and its closed form is derived. We introduce a measurement-driven adaptive birth model to initiate target tracks quickly and efficiently. Furthermore, a suboptimal Gibbs sampler is used to reduce the computational complexity of its practical implementation. The simulation results show that the proposed filter is resilient to unknown detection parameters and can effectively perform in challenging underwater scenarios.