A Particle PHD Filter for Multi-Sensor Multi-Target Tracking Based on Sequential Fusion

This paper presents a sequential filter implementation of particle Probability Hypothesis Density (PHD) filter for multisensor multi-target tracking. The tracking system involves potentially nonlinear target dynamics described by Markov state space model and nonlinear measurements. Each sensor reports measurements to the tracking system, which performs sequential estimation of the current state using the particle PHD filter, which propagates only the first order statistical moment of the full posterior of the multi-target state. Simulation results are also given and compared with a single radar multi-target tracking, showing the advantage of the fusion tracking over the single radar multi-target tracking.