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基于ML背景参数估计的CDKF-CPHD多目标跟踪算法

马天力 王新民 曹宇燕 张阳

马天力, 王新民, 曹宇燕, 等 . 基于ML背景参数估计的CDKF-CPHD多目标跟踪算法[J]. 北京航空航天大学学报, 2017, 43(3): 516-523. doi: 10.13700/j.bh.1001-5965.2016.0189
引用本文: 马天力, 王新民, 曹宇燕, 等 . 基于ML背景参数估计的CDKF-CPHD多目标跟踪算法[J]. 北京航空航天大学学报, 2017, 43(3): 516-523. doi: 10.13700/j.bh.1001-5965.2016.0189
MA Tianli, WANG Xinmin, CAO Yuyan, et al. A CDKF-CPHD multi-target tracking algorithm based on ML background parameter estimation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(3): 516-523. doi: 10.13700/j.bh.1001-5965.2016.0189(in Chinese)
Citation: MA Tianli, WANG Xinmin, CAO Yuyan, et al. A CDKF-CPHD multi-target tracking algorithm based on ML background parameter estimation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(3): 516-523. doi: 10.13700/j.bh.1001-5965.2016.0189(in Chinese)

基于ML背景参数估计的CDKF-CPHD多目标跟踪算法

doi: 10.13700/j.bh.1001-5965.2016.0189
基金项目: 

航空科学基金 20152853029

详细信息
    作者简介:

    马天力,男,博士研究生。主要研究方向:多目标跟踪、信息融合、信号处理

    王新民,男,博士,教授,博士生导师。主要研究方向:鲁棒控制理论、非线性控制理论、参数优化理论与方法、故障诊断与自修复

    通讯作者:

    王新民, E-mail:wxmin@nwpu.edu.cn

  • 中图分类号: TP273

A CDKF-CPHD multi-target tracking algorithm based on ML background parameter estimation

Funds: 

Aeronautical Science Foundation of China 20152853029

  • 摘要:

    针对低信杂比环境下的多机动目标跟踪问题,提出了一种基于极大似然(ML)背景参数估计的中心差分卡尔曼-势概率假设密度滤波(BE-CDKF-CPHD)算法。算法采用ML法实时估计重尾分布模型参数,计算检测概率和虚警概率。运用极大似然-恒虚警(ML-CFAR)算法对信号进行处理, 提取有效量测值, 将幅值似然函数与势概率假设密度滤波器(CPHD)中的目标位置似然函数相结合,通过中心差分法递归更新得到后验均值与协方差,达到对多机动目标进行跟踪的目的。仿真结果表明,在低信杂比环境中,所提算法提高了跟踪精度与目标数目估计准确度。

     

  • 图 1  目标运动轨迹

    Figure 1.  Target motion trajectories

    图 2  不同信杂比下的OSPA距离比较

    Figure 2.  Comparison of OSPA distance under different signal-to-clutter ratios

    图 3  3种算法的OSPA距离比较

    Figure 3.  Comparison of OSPA distance for three algorithms

    图 4  3种算法的目标数估计与真实目标数的比较

    Figure 4.  Comparison of true and estimated target numbers for three algorithms

    表  1  目标运动参数

    Table  1.   Target motion parameters

    目标 初始 (结束) 时刻/s 初始位置/m 初始速度/(m·s-1)
    1 2(40) (61, 30) (-1, 1)
    2 12(40) (64, 27) (-1.5, -1.5)
    3 12(50) (64, 30) (1, 1.5)
    下载: 导出CSV

    表  2  仿真环境

    Table  2.   Simulation environment

    情况 ν b δt SCR/dB
    1 2 0.5 2.5 10
    2 2 0.1 1 20
    3 1 0.1 20 30
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-03-10
  • 录用日期:  2016-06-12
  • 网络出版日期:  2017-03-20

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