低空监视雷达“走-停-走”目标跟踪技术

徐开明 王佰录 李溯琪 邓云凯 王经鹤

徐开明, 王佰录, 李溯琪, 等. 低空监视雷达“走-停-走”目标跟踪技术[J]. 雷达学报, 2022, 11(3): 443–458. doi: 10.12000/JR21211
引用本文: 徐开明, 王佰录, 李溯琪, 等. 低空监视雷达“走-停-走”目标跟踪技术[J]. 雷达学报, 2022, 11(3): 443–458. doi: 10.12000/JR21211
XU Kaiming, WANG Bailu, LI Suqi, et al. Move-stop-move target tracking with low-altitude surveillance radars[J]. Journal of Radars, 2022, 11(3): 443–458. doi: 10.12000/JR21211
Citation: XU Kaiming, WANG Bailu, LI Suqi, et al. Move-stop-move target tracking with low-altitude surveillance radars[J]. Journal of Radars, 2022, 11(3): 443–458. doi: 10.12000/JR21211

低空监视雷达“走-停-走”目标跟踪技术

doi: 10.12000/JR21211
基金项目: 国家自然科学基金(61901094),中央高校基金(2021CDJJMRH-009),重庆大学科研启动基金(02140011044134)
详细信息
    作者简介:

    徐开明(1987–),男,博士,现为中国科学院空天信息创新研究院助理研究员。主要研究方向为相控阵动目标雷达信号处理与数据处理、相控阵雷达系统设计。曾获得部级科技进步一等奖两项。发表论文2篇,申请专利4篇

    王佰录(1988–),男,重庆大学博士后,意大利佛罗伦萨大学访问学者。2018年在电子科技大学信息与通信工程学院获得博士学位。主要研究方向为分布式多传感器信息融合、5G/6G基站定位技术、雷达微弱目标检测跟踪等

    李溯琪(1990–),女,博士生导师,重庆大学弘深青年学者教授,意大利佛罗伦萨大学访问学者,博士后创新人才计划入选者。主要研究方向为基于随机集的多目标跟踪技术、分布式多智能体协同探测与信息融合技术、5G/6G基站感知技术等

    邓云凯(1962–),男,中科院特聘研究员、博士生导师。长期从事星载成像雷达系统设计、成像基础理论及微波遥感理论研究,曾获得国家科技进步一等奖、国家技术发明二等奖等奖项

    王经鹤(1991–),女,博士。现为中国科学院空天信息创新研究院助理研究员。主要研究方向为相控阵雷达系统设计、雷达数据处理及目标检测跟踪

    通讯作者:

    王佰录 wangbailu@cqu.edu.cn

  • 责任主编:陈小龙 Corresponding Editor: CHEN Xiaolong
  • 中图分类号: TN953

Move-stop-move Target Tracking with Low-altitude Surveillance Radars

Funds: The National Natural Science Foundation of China (61901094), The Fundamental Research Funds for the Central Universities (2021CDJJMRH-009), The Starting Research Fund from Chongqing University (02140011044134)
More Information
  • 摘要: 以旋翼无人机为代表的低空小目标常采用低速“走-停”策略或利用障碍物遮挡,躲避雷达追踪,对重要信息装备和战略要地进行点穴式打击或干扰。这类目标可多次消失-重返于雷达视域,称之“走-停-走”目标。若采用传统目标跟踪模型和算法处理这类目标,易导致目标身份不连续、航迹碎片化。该文在随机集理论框架下,基于标签多伯努利(LMB)滤波器,研究低空监视雷达“走-停-走”目标连续跟踪问题。为描述“走-停-走”目标多次往返于雷达视域的演化特性,首次引入第3类出生目标模型,即重生(RB)过程模型。首先,利用目标重返雷达视域前-后目标状态的空间位置和动力学参数关系,提出一种基于空域相关(SC)的RB过程;然后,基于SC-RB过程,在贝叶斯滤波框架下,设计了SC-RB-LMB滤波器算法,可实现多“走-停-走”目标连续稳健跟踪,维持航迹标签的连续性;最后,在典型低空监视场景下,通过仿真和实测数据验证了提出模型和算法的有效性和性能优势。

     

  • 图  1  “走-停-走”目标运动轨迹

    Figure  1.  Trajectories of move-stop-move targets

    图  2  多“走-停-走”目标状态转移过程

    Figure  2.  Markov transition of multiple move-stop-move targets

    图  3  死亡目标重生激活过程

    Figure  3.  The activation process of deaths

    图  4  SC-RB-LMB滤波器算法总体流程

    Figure  4.  The overall flow diagram of the SC-RB-LMB filter algorithm

    图  5  低空监视雷达传感器模型图

    Figure  5.  Sensor model of low-altitude surveillance radars

    图  6  雷达监视场景-3D视图多目标真实运动轨迹

    Figure  6.  The radar surveillance senario: 3D-ground truths

    图  7  单次蒙特卡洛实验2D目标航迹估计

    Figure  7.  2D target track estimation for single Monte-Carlo experiment

    图  8  不同算法跟踪性能对比

    Figure  8.  Tracking performance comparison among different algorithms

    图  9  计算时间性能对比

    Figure  9.  Comparison of execution times

    图  10  不同杂波率下SC-RB-LMB跟踪性能对比

    Figure  10.  Tracking performance comparison of SC-RB-LMB filter under different clutter rates

    图  11  不同检测概率下SC-RB-LMB跟踪性能对比

    Figure  11.  Tracking performance comparison of SC-RB-LMB filter under different detection probabilities

    图  12  无人机长时间折返运动场景-跟踪性能对比

    Figure  12.  Long palindrome path of the UAV- tracking performance comparison

    图  13  无人机短时间折返运动场景-跟踪性能对比

    Figure  13.  Short palindrome path of the UAV- tracking performance comparison

    表  1  不同目标的出生时刻和死亡时刻

    Table  1.   Time of births and deaths for different targets

    目标出生帧数死亡帧数起始位置(m)结束位置(m)平均速度(m/s)
    T11250(590,1000)(–106,877)(–20,–2)
    T21150(–250,560)(–95,1333)(3.5,12)
    T31500(–500,1200)(–372,1368)(18,0.5)
    T4200350(500,410)(525,1878)(0.2,18)
    T5151500(–500,1650)(–530,430)(–0.2,–18)
    T6200350(200,1210)(208,630)(0,20)
    T7151500(–300,800)(340,1570)(12,15)
    T8300400(42,1510)(–400,1865)(–17,18)
    T9351500(800,1600)(–175,527)(–12,–16)
    T10351500(–600,1650)(890,610)(18,–16)
    下载: 导出CSV

    表  2  “走-停-走”目标停止期

    Table  2.   Stopping period of move-stop-move targets

    目标停止帧数
    T166~73, 157~168
    T343~51, 168~176, 310~322, 396~412, 448~450
    T582~91
    T670~83, 171~180, 300~309
    T10106~114
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-30
  • 修回日期:  2022-03-29
  • 网络出版日期:  2022-04-25
  • 刊出日期:  2022-06-28

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