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

TL;DR

This paper introduces novel particle filter algorithms for multi-target tracking in distributed sensor networks, effectively handling data association issues and demonstrating robustness against clutter and outliers.

Contribution

It develops the Diffusion Particle PHD Filter and a centralized Multi-Sensor Particle PHD Filter, advancing multi-target tracking with improved robustness and performance evaluation methods.

Findings

The proposed filters outperform existing distributed PHD filters.

Robustness against outliers and clutter is demonstrated.

Performance is validated using OSPA metric and PCRLB benchmarks.

Abstract

Multi-target tracking is an important problem in civilian and military applications. This paper investigates multi-target 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 Cram\'er-Rao Lower Bound (PCRLB), and compared to the performance of an existing distributed PHD Particle Filter. Furthermore, the robustness of the proposed tracking algorithms against outliers and their performance with respect to different amounts of clutter is investigated.