计算机工程 ›› 2018, Vol. 44 ›› Issue (6): 316-320.doi: 10.19678/j.issn.1000-3428.0046745

• 开发研究与工程应用 • 上一篇    

针对交叉目标场景的带标签GM-PHD改进算法

陈金广,赵甜甜,王明明,王伟   

  1. 西安工程大学 计算机科学学院,西安 710048
  • 收稿日期:2017-04-11 出版日期:2018-06-15 发布日期:2018-06-15
  • 作者简介:陈金广(1977—),男,教授、博士,主研方向为信息融合、目标跟踪;赵甜甜,硕士研究生;王明明,讲师、博士;王伟,讲师、博士后。
  • 基金项目:

    国家自然科学基金(61601358);陕西省自然科学基础研究计划项目(2016JM6030);中国纺织工业联合会科技指导性项目(2016074);陕西省教育厅科研计划项目(15JK1291);西安工程大学研究生创新基金(CX201631)。

Improved Labeled GM-PHD Algorithm for Scenario with Crossing Target

CHEN Jinguang,ZHAO Tiantian,WANG Mingming,WANG Wei   

  1. School of Computer Science,Xi’an Polytechnic University,Xi’an 710048,China
  • Received:2017-04-11 Online:2018-06-15 Published:2018-06-15

摘要:

在多目标跟踪系统中,当目标航迹较为接近或交叉时,使用带标签高斯混合概率假设密度(GM-PHD)算法会出现目标漏检的现象。为此,提出一种改进算法来解决存在交叉目标情况下的多目标跟踪问题。在更新结束后对估计得到的高斯项标签进行管理,若估计目标数目减少,则 需要判断目标航迹是否较为接近或者交叉。若目标接近或交叉,则对高斯项进行标签管理和权值重置,并重新估计目标状态和航迹,否则将目标减少视为正常的目标消亡现象,直接进行航迹管理。实验结果表明,与无标签算法及常规带标签算法相比,该算法可以更好地解决由 目标交叉导致的漏检问题,并具有更高的稳定性。

关键词: 交叉目标, 目标跟踪, 概率假设密度滤波, 信息融合, 状态估计

Abstract:

In multi-target tracking system,some targets will be lost in the tracking when the targets are crossing or closed to each other used by Gaussian Mixture-Probability Hypothesis Density(GM-PHD).In order to solve this problem,an improved algorithm for scenario with crossing targets is put forward.After update,the estimated Gauss label is managed.If the estimated number of targets is reduced,it is needed to determine whether the targets are crossing or closed to each other.If these trajectories are closed to each other,the followed step is label management and weight reset of Gaussian items,and target states and tracks are re-estimated.Otherwise,the reduction of target will be regarded as a normal target extinction phenomenon,and track management is directly carried out.Experimental results show that the improved algorithm can solve the missed detection caused by the crossing targets successfully,and has good stability.

Key words: crossing target, target tracking, Probability Hypothesis Density(PHD) filtering, information fusion;state estimation

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