Distributed Multi-Target Tracking in Autonomous Sensor Networks

Multi-target tracking is an important problem in civilian and military applications. This paper investigates distributed multi-target tracking using autonomous sensor networks. Data association, which arises particularly in multi-object scenarios, can be tackled by various solutions. We consider sequential Monte Carlo implementations of the Probability Hypothesis Density (PHD) filter based on random finite sets. This approach circumvents the data association issue by jointly estimating all targets in the region of interest. To this end, we develop the Multi-Sensor Particle PHD Filter (MS-PPHDF) as well as a distributed version, called Diffusion Particle PHD Filter (D-PPHDF). Their performance is evaluated in terms of the Optimal Subpattern Assignment (OSPA) metric, benchmarked against a distributed extension of the Posterior Cramér-Rao Lower Bound (PCRLB), and compared to the performance of an existing distributed PHD Particle Filter.