A box-particle implementation of standard PHD filter for extended target tracking

Abstract

This paper presents a box-particle implementation of the standard probability hypothesis density (PHD) filter for extended target tracking, called the extended target box-particle PHD (ET-Box-PHD) filter. The proposed filter can dynamically track multiple extended targets and estimate the unknown number of extended targets, in the presence of clutter measurements, false alarms and missed detections, where the extended targets are described as a Poisson model developed by Gilholm et al. To get the PHD recursion of the ET-Box-PHD filter, a suitable cell likelihood function for one given reliable partition is derived, and the main filter steps are presented along with the necessary box manipulations and approximations. The capabilities and limitations of the proposed ET-Box-PHD filter are illustrated both in linear simulation examples and in nonlinear ones. The simulation results show that the proposed ET-Box-PHD filter can effectively avoid the high number of particles and obviously reduce computational burden, compared to a particle implementation of the standard PHD filter for extended target tracking.