上海交通大学学报

上海交通大学学报（自然版）

• 自动化技术、计算机技术 •    下一篇

一种新的多机动目标跟踪的GMPHD滤波算法

郝燕玲1，孟凡彬1，2，王素鑫2，孙枫1   

  1. （1.哈尔滨工程大学 自动化学院, 哈尔滨 150001； 2.天津航海仪器研究所， 天津 300131）
  • 收稿日期:2009-06-22 修回日期:1900-01-01 出版日期:2010-07-28 发布日期:2010-07-28

A New GMPHD Filter Algorithm for Multiple Maneuvering Targets Tracking

HAO Yanling1,MENG Fanbin1, 2,WANG Suxin2,SUN Feng1   

  1. （1.College of Automation, Harbin Engineering University, Harbin 150001, China; 2.Tianjin Navigation Instrument Research Institute, Tianjin 300131, China）
  • Received:2009-06-22 Revised:1900-01-01 Online:2010-07-28 Published:2010-07-28

摘要： 针对多机动目标跟踪的传统数据关联算法约束条件苛刻、估计精度低、计算量大等问题，提出了一种基于随机集理论的非数据关联的多机动目标跟踪算法.该算法将高斯混合概率假设密度（GMPHD）滤波与“当前”统计模型的优点相结合，绕过了棘手的数据关联问题，能高效处理目标数较大的机动跟踪问题.在漏检、虚警、多机动目标交叉杂波复杂环境下进行了仿真实验，结果表明，该算法具有较高的跟踪精度和稳健的跟踪性能.

关键词: 多机动目标跟踪, 随机有限集, 高斯混合概率假设密度滤波, 扩展卡尔曼滤波

Abstract: Considering the traditional data association algorithm of multiple maneuvering targets tracking being of hard constraint condition, lower estimated accuracy, and higher computational complexity, a non data association tracking algorithm based on the random set theory was proposed. Since the proposed algorithm integrates the both advantages of Gaussian mixture probability hypothesis density (GMPHD) filter and current statistical mode1, avoids the difficult problem of data association, it is able to deal with multiple maneuvering targets tracking effectively. A simulation experiment was performed in the complex environment with clutter, miss detection, false alarm, dense, and cross targets. The simulation results show that the proposed algorithm has higher tracking accuracy and more steady tracking performance.

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