Tracking Ground Targets with Road Constraint Using a Gaussian Mixture Road-Labeled PHD Filter

Abstract

The general focus of this paper is the improvement of state-of-the-art Bayesian tracking filters specialized to the domain of ground moving target tracking to obtain high-quality track information by incorporation of road-map information into a Gaussian mixture probability hypothesis density (GM-PHD) filtering scheme. In this paper, we propose a road-labeled GM-PHD (GM-RL-PHD) filter for ground targets with road-segment constrained dynamics and the recursive equations of the filter is derived. The proposed filter is validated with a ground target tracking example. The simulation results show that the proposed algorithm can improve the performance of ground target tracking algorithm by fusing road map information.