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粒子PHD滤波存活粒子采样新方法 被引量：1

New method for survive particle sampling of particle PHD filter
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摘要 针对多目标跟踪粒子概率假设密度滤波算法中存活粒子的重要性密度采样问题,给出一种结合最新量测信息的存活粒子重要性密度采样新方法。该方法根据最新量测集中的各个量测与目标粒子的单步预测状态的似然值,以概率选取量测值,利用无迹变换获得粒子的重要性密度函数,并对其进行采样实现粒子概率假设密度滤波中存活粒子的采样,有效地减轻了粒子的退化现象。3目标跟踪仿真试验中,当目标模型与跟踪算法使用的目标模型不匹配时,采用所提出的存活粒子采样方法的粒子概率假设密度滤波算法最优子模式分配距离下降约70km。论文给出的存活粒子采样新方法显著地提高了多目标跟踪粒子概率假设密度滤波算法的鲁棒性。 For the problem of survive particle importance sampling in multitarget tracking Probability Hypothesis Density particle filter,a new algorithm of survive particle importance sampling is presented.For every particle,the algorithm exploits likelihood between latest measurements,and the particle chooses one measurement of the set of measurement to obtain importance distribution by update particle,and draws samples of survive particle from the importance distribution.The presented algorithm reduced degeneration of particle efficiently.In simulation scenario of 3 targets tracking,the optimal sub-pattern assignment metric of particle probability hypothesis density filter,which adopted the presented survive particle importance sample method,decreases about 70Km when targets model used in target tracking method is different from actual targets model.The proposed method enhances the robustness of multitarget tracking of particle probability hypothesis density filter remarkably.
作者 罗少华 徐晖 安玮 许丹 张志恒
机构地区 国防科技大学电子科学与工程学院 [
出处 《国防科技大学学报》 EI CAS CSCD 北大核心 2012年第2期102-106,共5页 Journal of National University of Defense Technology
基金 国家部委资助项目
关键词 概率假设密度滤波 粒子采样 多目标跟踪 无迹变换 particle probability hypothesis density particle sample multi-target tracking unscented transformation
分类号 TN911.7 [电子电信—通信与信息系统]
  • 相关文献

参考文献14

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二级参考文献12

  • 1彭冬亮,文成林,徐晓滨,薛安克.随机集理论及其在信息融合中的应用[J].电子与信息学报,2006,28(11):2199-2204. 被引量：24
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共引文献10

同被引文献11

  • 1Blackman S,Popoli R.Design and analysis of modern tracking systems[M].London:Artech House,1999:7-12.
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  • 4Mahler R.Multitarget Bayes filtering via first order multitarget moments[J].IEEE Trans on Aerospace and Electronic Systems,2003,39(4):1152-1178.
  • 5Mahler R.Statistical multisource-multitarget information fusion[M].Boston:Artech House,2007:711-715.
  • 6Vo B N,Singh S,Doucet A.Sequential Monte Carlo methods for multi-target filtering with random finite sets[J].IEEE Trans on Aerospace and Electronic Systems,2005,41(4):1224-1245.
  • 7Clark D E,Bell J.Multi-target state estimation and track continuity for the particle PHD filter[J].IEEE Trans on Aerospace and Electronic Systems,2007,43(4):1441-1453.
  • 8Tang X,Wei P.Multi-target state extraction for the particle probability hypothesis density filter[J].IET Radar,Sonar and Navigation,2010,5(8):877-883.
  • 9Zhao Lingling,Ma Peijun,Su Xiaohong.A new multitarget estimation algorithm for PHD particle filter[C]//IEEE Information Fusion 13th Conference,2010:1-8.
  • 10Schuhmacher D,Vo B T,Vo B N.A consistent metric for performance evaluation of multi-object filters[J].IEEE Trans on Signal Process,2008,56(8):3447-3457.

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国防科技大学学报
2012年 第2期

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