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Multi-target tracking is an important problem in civilian and militaryapplications. This paper investigates multi-target tracking in distributedsensor networks. Data association, which arises particularly in multi-objectscenarios, can be tackled by various solutions. We consider sequential MonteCarlo implementations of the Probability Hypothesis Density (PHD) filter basedon random finite sets. This approach circumvents the data association issue byjointly estimating all targets in the region of interest. To this end, wedevelop the Diffusion Particle PHD Filter (D-PPHDF) as well as a centralizedversion, called the Multi-Sensor Particle PHD Filter (MS-PPHDF). Theirperformance is evaluated in terms of the Optimal Subpattern Assignment (OSPA)metric, benchmarked against a distributed extension of the PosteriorCram\'er-Rao Lower Bound (PCRLB), and compared to the performance of anexisting distributed PHD Particle Filter. Furthermore, the robustness of theproposed tracking algorithms against outliers and their performance withrespect to different amounts of clutter is investigated.

Multi-Target Tracking in Distributed Sensor Networks using Particle PHD Filters