分布式多传感器多目标跟踪方法综述

曾雅俊 王俊 魏少明 孙进平 雷鹏

曾雅俊, 王俊, 魏少明, 等. 分布式多传感器多目标跟踪方法综述[J]. 雷达学报, 待出版. doi: 10.12000/JR22111
引用本文: 曾雅俊, 王俊, 魏少明, 等. 分布式多传感器多目标跟踪方法综述[J]. 雷达学报, 待出版. doi: 10.12000/JR22111
ZENG Yajun, WANG Jun, WEI Shaoming, et al. Review of the method for distributed multi-sensor multi-target tracking[J]. Journal of Radars, in press. doi: 10.12000/JR22111
Citation: ZENG Yajun, WANG Jun, WEI Shaoming, et al. Review of the method for distributed multi-sensor multi-target tracking[J]. Journal of Radars, in press. doi: 10.12000/JR22111

分布式多传感器多目标跟踪方法综述

doi: 10.12000/JR22111
基金项目: 国家自然科学基金(62171029, 61671035),预研基金(61404130122),重点实验室基金(6142502180103),教育部产学合作协同育人项目(202101105001)
详细信息
    作者简介:

    曾雅俊,博士生,主要研究方向为多目标跟踪、多源信息融合

    王 俊,博士,教授,研究方向为雷达信号处理、FPGA/DSP嵌入式系统、目标识别与跟踪、多传感器数据融合

    魏少明,博士,实验师,研究方向为雷达信号处理、多目标跟踪、数据融合、三维成像

    孙进平,教授,博士生导师,研究方向为目标跟踪、信号分析检测与估计、稀疏微波成像、图像理解、雷达信号与数据处理的算法及软硬件实现

    雷 鹏,博士,副教授,硕士生导师,主要研究方向为数字信号处理、贝叶斯估计、模式识别

    通讯作者:

    魏少明 shaoming.wei@buaa.edu.cn

  • 责任主编:关键 Corresponding Editor: GUAN Jian
  • 中图分类号: TN951; TN957.51; TN971.+1

Review of the Method for Distributed Multi-sensor Multi-target Tracking

Funds: The National Natural Science Foundation of China (62171029, 61671035), The Pre-research Foundation (61404130122), The Key Laboratory Foundation (6142502180103), The Ministry of Education’s Industry-University Cooperation and Collaborative Education Project (202101105001)
More Information
  • 摘要: 多传感器多目标跟踪是信息融合领域的热点问题,其通过融合多个局部传感器数据,提高目标跟踪精度和稳定性。多传感器多目标跟踪按融合体系可分为分布式、集中式、混合式3类,其中分布式融合结构对网络通信带宽要求低、可靠性和稳定性强,广泛应用于军事、民用领域。该文聚焦分布式多传感器多目标跟踪涉及的目标跟踪、传感器配准、航迹关联、数据融合4项关键技术,主要分析了各关键技术的理论原理与适用条件,重点介绍了不完整测量条件下的空间配准与航迹关联,并给出仿真结果。最后,该文总结了现有分布式多传感器多目标跟踪关键技术存在的问题,并指出了其未来发展趋势。

     

  • 图  1  分布式多传感器多目标跟踪流程图

    Figure  1.  Flowchart of distributed multi-sensor multi-target tracking

    图  2  时间配准方法

    Figure  2.  Time registration methods

    图  3  空间配准几何示意图

    Figure  3.  Illustration of spatial registration

    图  4  空间配准场景

    Figure  4.  Scene of spatial registration

    图  5  WGS84坐标系下配准前后显著性目标位置[82]

    Figure  5.  Registration results before and after registration in the WGS84 coordinate system[82]

    图  6  典型的航迹关联方法及分类

    Figure  6.  Classification of track-to-track association methods

    图  7  航迹关联场景

    Figure  7.  Scene of track-to-track association

    图  8  航迹关联正确率[82]

    Figure  8.  Accuracy of track association[82]

    图  9  航迹关联与空间配准关系[82]

    Figure  9.  Relationship of track-track association and spatial registration[82]

    图  10  分布式多传感器估计融合

    Figure  10.  Distributed multi-sensor estimation fusion

    表  1  典型的多目标跟踪方法性能对比

    Table  1.   Performance comparison of different multi-target tracking methods

    多目标跟踪类型跟踪方法跟踪精度运算量
    数据关联类GNN
    JPDA中等中等
    JIPDA中等中等
    MHT
    随机有限集类PHD
    CPHD中等中等
    TPHD中等
    TCPHD中等中等
    MeMBer中等中等
    PMBM中等
    GLMB
    LMB中等
    下载: 导出CSV

    表  2  多传感器时间配准方法性能对比

    Table  2.   Comparison of multi-sensor time registration methods

    配准类型配准方法配准精度计算量目标运动状态
    插值类内插外推较低匀速
    曲线插值中等较高匀速\非匀速
    曲线拟合中等较高匀速\非匀速
    参数估计类最小二乘中等中等匀速
    卡尔曼滤波较高较高匀速\非匀速
    下载: 导出CSV

    表  3  多传感器非合作目标空间配准方法分类

    Table  3.   Classification of multi-sensor spatial registration methods based on non-cooperative targets

    配准方法实时性是否能估计
    目标位置
    参与传感器
    个数
    传感器类型
    RTQC离线两个同类
    LS离线两个同类
    GLS离线两个同类
    EML离线两个同类
    KF在线多个同类
    RFS在线多个同类
    MLR离线多个同类\异类
    RBER离线多个同类\异类
    下载: 导出CSV

    表  4  航迹关联性能对比

    Table  4.   Comparison of multi-sensor track-to-track association methods

    航迹关联方法航迹关联正确率(%)时间开销(s)
    SMBTANTD[82]99.63.4
    Generalized Likelihood[15]97.42.8
    Fuzzy function[89]96.78.1
    下载: 导出CSV

    表  5  多传感器估计融合方法对比

    Table  5.   Comparison of multi-sensor estimation fusion methods

    估计融合方法是否考虑
    航迹相关
    计算量融合
    精度
    传感器
    类型
    简单凸组合融合同类
    Bar-Shalom-Campo融合较高较高同类
    基于MAP较高较高同类
    CI融合较高较高同类
    GCI融合较高较高同类/异类
    AA融合较高较高同类/异类
    基于EKF融合较高较高同类/异类
    基于UKF融合较高较高同类/异类
    基于PF融合同类/异类
    下载: 导出CSV

    表  6  典型的多传感器多目标跟踪方法性能对比

    Table  6.   Performance comparison of multi-sensor multi-target tracking methods

    多目标跟踪类型融合准则跟踪方法运算量跟踪精度
    数据关联类CI融合JPDA中等中等
    简单凸组合融合MHT中等
    随机有限集类GCI/GA融合PHD
    CPHD中等中等
    MeMBer中等中等
    GLMB
    LMB中等
    AA融合PHD
    CPHD中等中等
    MeMBer中等中等
    GLMB
    LMB中等
    下载: 导出CSV
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