关键词: 多目标跟踪, 高斯混合概率假设密度, 漏检, 分量合并

Abstract: The Gaussian mixture probability hypothesis density filter is an algorithm for estimating multiple target states in clutter. An improved algorithm is proposed to resolve the missed detection problem and enhance the accuracy of the filter while tracking close proximity targets. Under Gaussian mixture assumptions, the predication and update equations of the PHD filter are modified, which effectively solve the information loss problem of missed true targets. And then depending on the weights of Gaussian components which decide whether the components can be utilized to extract states, the proposed algorithm avoids the components which have higher weights are merged and improves the tracking performance when the targets move closely. Simulation results show that the new algorithm has advantages over the ordinary one in both the aspects of filter precision and multi-target number estimation.

Key words: multi-target tracking, Gaussian mixture probability hypothesis density filter, missed detection, component merging