关键词: 被动多目标跟踪, 粒子滤波, 概率假设密度, 数据关联

Abstract:

Multiple-target tracking based on passive measurements with clutters is a difficult problem. Multiple-senor centralized fusion scheme and paticle filter(PF)implementation for the probability hypothesis density(PHD)filter are combined to tracke multiple targets effectively. Furthermore, to sovle the incapablity of the PHD filter in maintaining the integrated trajectory of each traget, the PHD recursion is implemted in parallel, where each target is tracked with a single PF. A fast data association algorithm is deducted, in which the clustering for all particles is avoided. Simulation results show that, compared with the conventional ones, the new method can keep each tracks better without additonal computational costs. Especially, when targets cross each other, the respective trajectories can be distinguished effectively.

Key words: passive multi-target tracking, particle filter, probability hypothesis density(PHD), data association