电子学报

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一种基于模糊聚类的PHD航迹维持算法

欧阳成, 姬红兵, 田野   

  1. 西安电子科技大学电子工程学院,陕西西安 710071
  • 收稿日期:2010-06-01 修回日期:2011-11-10 出版日期:2012-06-25 发布日期:2012-06-25
  • 作者简介:欧阳成 男,1985年生,博士生,被动多传感器定位与跟踪,信息融合.E-mail:ouoyc@yahoo.com.cn
    姬红兵 男,1963年生,教授,博士生导师,光电信息处理、智能信息处理、被动多传感器定位与跟踪、雷达目标识别与分类、微弱信号检测与识别、医学影像处理等.
    田 野 男,1986年生,硕士生,信号处理与检测,目标跟踪.
  • 基金资助:

    国家自然科学基金(No.60871074)

Fuzzy Clustering Based Algorithm for Track Continuity in PHD Filter

OUYANG Cheng, JI Hong-bing, TIAN Ye   

  1. School of Electronic Engineering,Xidian University,Xi'an,Shannxi 710071,China
  • Received:2010-06-01 Revised:2011-11-10 Online:2012-06-25 Published:2012-06-25

摘要: 针对杂波环境下数量变化的多目标航迹关联问题,提出一种基于模糊聚类的PHD航迹维持算法.该算法充分利用多帧信息,对当前时刻状态进行多步预测,并根据惯性进行加权,然后利用模糊聚类求得当前估计属于每条航迹的隶属度,从而得到最终的航迹.与传统的估计与航迹关联算法不同,该算法在更新每条航迹信息时,不仅仅是简单地对相邻帧之间的对数似然比进行求和,而是通过加权聚类等操作综合考虑了多帧信息.实验结果表明,所提算法能够更好地保持目标航迹,即使在目标出现交叉的地方也能达到很好的跟踪精度,具有较强的鲁棒性和优良的航迹维持性能.

关键词: 模糊聚类, 概率假设密度滤波, 数据关联, 航迹维持

Abstract: Due to the difficulty in association and estimation of multi-target track in the presence of data association uncertainty,clutter,noise and miss-detection,a fuzzy clustering based algorithm for track continuity in probability hypothesis density (PHD) filter is proposed in this paper.Firstly,a multi-step prediction of current target states is made,and then the weighted labels are assigned to them according to the inertia.Secondly,the fuzzy membership degrees of the current state estimates belonging to the tracks are obtained with the maximum entropy fuzzy clustering.Finally,the tracks are maintained by the use of all the information.Different from the traditional estimate-to-track association,the proposed algorithm does not update the track information by simply summing the log likelihood ratios between adjacent frames,but takes the entire multi-frame information into account by the operations such as weighting and clustering.The simulation results show that the proposed algorithm can maintain target tracks more accurately,even when the targets cross each other,implying strong robustness and excellent performance of track continuity.

Key words: fuzzy clustering, probability hypothesis density (PHD) filter, data association, track continuity

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