A Gaussian Mixture PHD Filter for Multitarget Tracking in Target-Dependent False Alarms

Tracking individuals within a group is one of the major tasks of group target observation. Tracking radar must feature high frame rate and high range-angular resolution to achieve the stable multitarget tracking performance. However, two major problems arise from this scenario. First, the narrow beam of tracking radar does not allow the complete observation of group target, causing the fluctuation of target number as the radar-target geometry changes; second, false alarms may be target-dependent and distributed around the targets, which is contrary to the traditional spatially uniform clutter model. This paper proposes a Gaussian mixture probability hypothesis density filter for multitarget tracking using a collaborative radar system. The system consists of one scanning radar and one tracking radar. The former outputs the group's collective states (centroid, extension, etc.), which are used as the priors for the tracking radar. The tracking radar is responsible for the multitarget tracking. The density of target birth and death are set according to the priors. The update equation of the probability hypothesis density in target-dependent false alarms is derived and simplified to meet the practical application requirements. Finally, the effectiveness of the proposed filter is verified by the simulation and experimental results.