关键词: 概率假设密度粒子滤波, 多目标跟踪, 状态提取

Abstract: The Particle Probability Hypothesis Density filter（P-PHD） has emerged as an effective way to solve the multi-target tracking problems. Multi-target states are expressed in a series of particles with weights. It is of importance to estimate the target state from those particles in the multi-target tracking procedure. A new method to extract target states without the need of clustering is proposed. The updating step of the P-PHD is decomposed. The observation categories coming of the real-target are selected and the chosen observation categories are assigned with the same number of new particles with new weights respectively. The target state is extracted from those brand new particle clouds directly and there is no need to execute the clustering and peak extraction operation. The simulation results demonstrate that the proposed algorithm has a better performance than the k-means method.

Key words: Probability Hypothesis Density（PHD） particle filter, multi-target tracking, state extraction