西安电子科技大学学报 ›› 2021, Vol. 48 ›› Issue (2): 27-34.doi: 10.19665/j.issn1001-2400.2021.02.004

• 雷达技术进展专题 • 上一篇    下一篇

一种改进的泊松点过程概率多假设跟踪方法

张逸宸(),水鹏朗()   

  1. 西安电子科技大学 雷达信号处理国家重点实验室,陕西 西安 710071
  • 收稿日期:2020-10-01 修回日期:2020-12-09 出版日期:2021-04-20 发布日期:2021-04-28
  • 通讯作者: 水鹏朗
  • 作者简介:张逸宸(1995—),男,西安电子科技大学博士研究生,E-mail: yichenzhang@stu.xidian.edu.cn|张逸宸(1995—),男,西安电子科技大学博士研究生,E-mail: yichenzhang@stu.xidian.edu.cn
  • 基金资助:
    国家自然科学基金(62071346);国家自然科学基金(61871303);国家自然科学基金(62071346);国家自然科学基金(61871303)

Improved probabilistic multi-hypothesis tracker via the Poisson point process

ZHANG Yichen(),SHUI Penglang()   

  1. National Laboratory of Radar Signal Processing,Xidian University,Xi’an 710071,China
  • Received:2020-10-01 Revised:2020-12-09 Online:2021-04-20 Published:2021-04-28
  • Contact: Penglang SHUI

摘要:

多目标跟踪方法的核心是获取量测点与目标间的最优关联。传统的多目标跟踪方法从穷举出的所有可能关联中估计出最优关联,随目标数目指数倍增的关联复杂度制约了雷达跟踪多目标的能力。因此提出了一种低运算复杂度、高跟踪精度的多目标跟踪算法和与其配套的航迹管理方法,目标和杂波产生的量测点被建模为泊松点过程,以量测的来源为缺失信息,通过EM(Expectation Maximisation)算法迭代求解目标状态,独立的量测关联和目标混合概率大大降低了算法的复杂度。此外,额外的回波多普勒信息作为目标特征被引入关联和滤波环节辅助跟踪,提升了算法区分量测来源的能力,获得了更高的跟踪精度。实验结果表明,所提方法可以实现稳健的目标跟踪,且运算时间随目标数目线性增加。

关键词: 概率多假设跟踪, 目标特征辅助跟踪, 多目标跟踪, 目标跟踪, 跟踪系统, 概率多假设跟踪, 目标特征辅助跟踪, 多目标跟踪, 目标跟踪, 跟踪系统

Abstract:

Optimal data association is the main task of multi-target tracking due to the similarity of the tracker’s filtering parts.Traditional Multi-target tracking methods pick up the optimal data association from all possible associations that account for the complexity exponentially increasing with the number of targets and limiting the maximum number of targets which can be stably tracked.This paper proposes an efficient and accurate method where the measurement points raised by targets and clutter are modeled as the Poisson point process and the expectation maximisation algorithm is utilized to estimate the target states recursively.Independent data association and mixing probability decrease the computational complexity.Furthermore,Doppler information refers to the fact that the target feature has been used in association and filtering stage to improve tracking performance without adding complexity.The experiment with simulation data show that the performance of the proposed method is better than that of the traditional method with a shorter operation time.

Key words: probabilistic multi-hypothesis tracker, feature-aided tracking, multi-target tracking, target tracking, tracking system, probabilistic multi-hypothesis tracker, feature-aided tracking, multi-target tracking, target tracking, tracking system

中图分类号: 

  • TN820.4