A novel track maintenance algorithm for PHD/CPHD filter

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• 作者：Jinlong Yang ^{yjlgedeng@163.com} ; Hongbing Ji ; ^{hbji@xidian.edu.cn}
• 关键词：Probability hypothesis density filter ; Connectivity graph ; Cross entropy ; Track maintenance ; Rao-Blackwellized particle filter
• 刊名：Signal Processing
• 出版年：2012
• 出版时间：October, 2012
• 年：2012
• 卷：92
• 期：10
• 页码：2371-2380
• 全文大小：340 K

文摘

Probability hypothesis density (PHD) filter and cardinalized PHD (CPHD) filter have proved to be promising algorithms for tracking an unknown number of targets in real time. However, they do not provide the identities of the individual estimated targets, so the target tracks cannot be obtained. To solve this problem, we propose a new track maintenance algorithm based on the cross entropy (CE) technique. Firstly, the particle filter PHD (PF-PHD) algorithm is used to estimate the target states and the target number. Then, the results of the estimation are used as vertexes to construct a connectivity graph with associated weights, and the CE technique is employed as a global optimization scheme to calculate the optimal feasible associated events. Furthermore, due to the advantages of the CPHD filter and the Rao-Blackwellized particle filter (RBPF), we propose another track maintenance algorithm based on the CE technique, named the RBPF-CPHD tracker, which can further improve the track maintenance performance due to the more accurate state estimates and their number estimates. Simulation results show that the proposed algorithms can effectively achieve the track continuity, with stronger robustness and greater anti-jamming capability.