Tracking Ground Targets with Road Constraints Using a JMS-GM-PHD Filter
Jihong Zheng, He He, Longteng Cong · 2021
The probability hypothesis density filter with linear Gaussian jump Markov system multi-target models is an attractive approach to tracking multiple maneuvering targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. However, these models are not precise enough to describe moving targets on road networks in ground target tracking scenario. In this paper, the road map information is integrated into the jump Markov system Gaussian mixture probability hypothesis density (JMS-GM-PHD) filter, and a road-constraint JMS-GM-PHD filter for ground target tracking is proposed. In addition, we then derive the recursive equation of the proposed filter. Simulation results show that the proposed road-constrained JMS-GM-PHD filter is effective in tracking ground moving targets.