Abstract-Multi-target tracking is an important problem in civilian and military applications. This paper investigates multitarget tracking in distributed 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 Diffusion Particle PHD Filter (D-PPHDF) as well as a centralized version, called the Multi-Sensor Particle PHD Filter (MS-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.
More recently, Leonard and Zoubir REF developed a distributed particle filter implementation of the PHD filter for multitarget tracking.
Multi-Target Tracking in Distributed Sensor Networks using Particle PHD Filters
arXiv: Multiagent Systems
Mathematics Computer Science