航空学报 > 2015, Vol. 36 Issue (12): 3957-3969   doi: 10.7527/S1000-6893.2015.0104

新生目标强度未知的双门限粒子PHD滤波器

徐从安1, 刘瑜1, 熊伟1, 宋瑞华1, 李天梅2   

  1. 1. 海军航空工程学院信息融合研究所, 烟台 264001;
    2. 第二炮兵工程大学302教研室, 西安 710025
  • 收稿日期:2015-01-21 修回日期:2015-04-12 出版日期:2015-12-15 发布日期:2015-04-24
  • 通讯作者: 徐从安,Tel.:0535-6635877,E-mail:xcatougao@163.com E-mail:xcatougao@163.com
  • 作者简介:徐从安,男,博士,讲师。主要研究方向:多目标跟踪、随机集理论。Tel:0535-6635877,E-mail:xcatougao@163.com;刘瑜,男,博士,讲师。主要研究方向:非线性滤波、多传感器信息融合。Tel:0535-6635877,E-mail:liuyu77360132@126.com;熊伟,男,博士,教授,硕士生导师。主要研究方向:多传感器信息融合、系统仿真。Tel:0535-6635807,E-mail:xiongweimail@tom.com;宋瑞华,男,学士,工程师。主要研究方向:系统建模仿真。Tel:0535-6635877,E-mail:442838894@qq.com;李天梅,女,博士,讲师。主要研究方向:测试性设计、系统建模仿真,E-mail:tmlixjtu@163.com
  • 基金资助:

    国家自然科学基金(61471383);装备预研基金(9140A07030514JB14001);山东省自然科学基金(ZR2012FQ004)

A dual threshold particle PHD filter with unknown target birth intensity

XU Congan1, LIU Yu1, XIONG Wei1, SONG Ruihua1, LI Tianmei2   

  1. 1. Research Institute of Information Fusion, Naval Aeronautical and Astronautical University, Yantai 264001, China;
    2. 302 Unit., Second Artillery Engineering University, Xi'an 710025, China
  • Received:2015-01-21 Revised:2015-04-12 Online:2015-12-15 Published:2015-04-24
  • Supported by:

    National Natural Science Foundation of China (61471383); Equipment Pre-research Foundation (9140A07030514JB14001); Natural Science Foundation of Shandong Province (ZR2012FQ004)

摘要:

传统粒子概率假设密度(PHD)滤波器假定新生目标强度已知,当新生目标在整个观测区域随机出现时不再适用。为解决新生目标强度未知时的多目标跟踪问题,提出了一种基于量测信息的双门限粒子PHD(PHD-DT)滤波器。首先基于似然函数设定门限对存活目标量测进行粗提取,利用上一时刻的目标估计值构建圆形波门进行精细提取,并对门限设定方法进行分析,然后根据提取结果对目标PHD进行分解,得到存活目标和新生目标的PHD预测及更新表达式,最后给出了滤波器的实现方法并同基于量测驱动的PHD(PHD-M)滤波器和Logic+联合概率数据互联(JPDA)方法进行了仿真对比。仿真结果表明,在新生目标强度未知时,PHD-DT可有效避免Logic+JPDA在杂波背景下因航迹起始错误带来的估计误差,并较好地解决了PHD-M的目标数目过估问题,多目标估计性能更优,且杂波越强性能优势越明显。

关键词: 多目标跟踪, 概率假设密度, 新生目标强度未知, 似然函数, 双门限

Abstract:

In situations where the targets can appear anywhere in the surveillance region, the traditional probability hypothesis density (PHD), which assumes that the target birth intensity is known a priori, is inefficient any more. To overcome this problem, a dual threshold particle PHD (PHD-DT) filter with unknown target birth intensity is proposed. First, through an analysis of threshold selection, the threshold based on likelihood function is designed for measurement crude extraction and the circular threshold based on the last state estimation is set for refined extraction. Afterwards, according to the extraction results, the predication and update equations for persistent and newborn targets are derived by the decomposition of PHD. Finally, the implementation of the PHD-DT filter is presented. The simulation results show that PHD-DT filter, which avoids the track initiation error of Logic+JPDA and succeeds in reducing the overestimate problem of PHD-M, possesses higher estimation performance. Moreover, the stronger the clutters, the more significant performance advantage of PHD-DT filter.

Key words: multi-target tracking, probability hypothesis density, unknown target birth intensity, likehood function dual threshold

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