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Free clustering optimal particle probability hypothesis density(PHD) filter

Free clustering optimal particle probability hypothesis density(PHD) filter
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摘要 As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algorithm is used to extract target states,a free clustering optimal P-PHD(FCO-P-PHD) filter is proposed.This method can lead to obtainment of analytical form of optimal sampling density of P-PHD filter and realization of optimal P-PHD filter without use of clustering algorithms in extraction target states.Besides,as sate extraction method in FCO-P-PHD filter is coupled with the process of obtaining analytical form for optimal sampling density,through decoupling process,a new single-sensor free clustering state extraction method is proposed.By combining this method with standard P-PHD filter,FC-P-PHD filter can be obtained,which significantly improves the tracking performance of P-PHD filter.In the end,the effectiveness of proposed algorithms and their advantages over other algorithms are validated through several simulation experiments. As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density（P-PHD） filter would decline when clustering algorithm is used to extract target states,a free clustering optimal P-PHD（FCO-P-PHD） filter is proposed.This method can lead to obtainment of analytical form of optimal sampling density of P-PHD filter and realization of optimal P-PHD filter without use of clustering algorithms in extraction target states.Besides,as sate extraction method in FCO-P-PHD filter is coupled with the process of obtaining analytical form for optimal sampling density,through decoupling process,a new single-sensor free clustering state extraction method is proposed.By combining this method with standard P-PHD filter,FC-P-PHD filter can be obtained,which significantly improves the tracking performance of P-PHD filter.In the end,the effectiveness of proposed algorithms and their advantages over other algorithms are validated through several simulation experiments.
作者 李云湘 肖怀铁 宋志勇 范红旗 付强
机构地区 Science and Technology on Automatic Target Recognition Laboratory
出处 《Journal of Central South University》 SCIE EI CAS 2014年第7期2673-2683,共11页 中南大学学报（英文版）
关键词 采样密度 滤波器 准粒子 概率 PHD 聚类算法 提取方法 集群 multiple target tracking probability hypothesis density filter optimal sampling density particle filter random finite set clustering algorithm state extraction
分类号 TN713 [电子电信—电路与系统]
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参考文献23

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Journal of Central South University
2014年 第7期

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