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仅测角机动目标跟踪原始对偶高斯粒子滤波

张宏伟

张宏伟. 仅测角机动目标跟踪原始对偶高斯粒子滤波[J]. 电子与信息学报, 2024, 46(4): 1408-1417. doi: 10.11999/JEIT230413
引用本文: 张宏伟. 仅测角机动目标跟踪原始对偶高斯粒子滤波[J]. 电子与信息学报, 2024, 46(4): 1408-1417. doi: 10.11999/JEIT230413
ZHANG Hongwei. Angle-only Maneuvering Target Tracking Using Primal-dual Gaussian Particle Filtering[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1408-1417. doi: 10.11999/JEIT230413
Citation: ZHANG Hongwei. Angle-only Maneuvering Target Tracking Using Primal-dual Gaussian Particle Filtering[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1408-1417. doi: 10.11999/JEIT230413

仅测角机动目标跟踪原始对偶高斯粒子滤波

doi: 10.11999/JEIT230413
基金项目: 广东省基础与应用基础研究基金(2019A1515111099),中山大学青年培育项目(20lgpy72),中国科学院空间精密测量重点实验室开放基金(SPMT2021002, SPMT2022001)
详细信息
    作者简介:

    张宏伟:女,副研究员,博士,研究方向为目标跟踪、智能信息处理

    通讯作者:

    张宏伟 zhanghw69@mail.sysu.edu.cn

  • 中图分类号: TN953

Angle-only Maneuvering Target Tracking Using Primal-dual Gaussian Particle Filtering

Funds: Guang Dong Basic and Applied Fundamental Research Fund Project (2019A1515111099), Sun Yat-sen University Youth Cultivation Project (20lgpy72), The Open Research Fund of CAS Key Laboratory of Space Precision Measurement Technology (SPMT2021002, SPMT2022001)
  • 摘要: 为消减仅测角机动目标跟踪系统中由时空不一致引起的投影基点偏移和高斯截断两类误差,该文采用映射表示和$ {\ell _1} $-$ {\ell _{2,1}} $稀疏正则表征时空因果一致约束,引入模糊综合贴近度建立次优建议分布,构建因果不变结构传递粒子集合以近似目标后验高斯积分,推导原始对偶高斯粒子滤波(PDGPF)算法。实验结果表明,相比交会测量最小二乘法,PDGPF算法定位旋翼无人机(UAV)的精度提升了18.4%~69.6%。相比于软约束辅助粒子滤波(SCAPF)算法,PDGPF算法在时空映射一致约束下能够自适应地修正粒子的权值,从而更为准确、稳定地跟踪机动点目标,整体计算负担减小了12.9%。
  • 图  1  双站方向盘测角系统交会测量定位旋翼无人机

    图  2  跟踪目标在3轴上的投影距离

    图  3  高斯过程法仿真机动点目标航迹

    图  4  双站测角传感器测量的方位和俯仰角度

    图  5  3种跟踪算法滤波性能比较

    表  1  交会测量最小二乘法和PDGPF算法定位旋翼无人机在3轴上的投影误差

    交会测量最小二乘法PDGPF算法
    最大值 (m)方差 (m2)最大值(m)方差 (m2)
    $ x $轴4.851.184.511.10
    $ y $轴11.864.126.041.60
    $ z $轴9.333.953.351.20
    下载: 导出CSV

    表  2  100轮蒙特卡罗实验统计滤波误差(均值、协方差)和1轮蒙特卡罗实验运行时间

    算法位置均方根误差$ x $轴上滤波误差$ y $轴上滤波误差$ z $轴上滤波误差运行时间 (s)
    均值 (m)协方差 (m2)最大值 (m)协方差 (m2)最大值 (m)协方差 (m2)最大值 (m)协方差 (m2)
    RGPMT32.3227.7445.2512.7198.0031.9939.2811.420.118
    SCAPF18.8215.5924.506.53–50.5315.7624.586.161.442
    PDGPF10.729.1412.063.33–30.617.7615.763.651.278
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-05-15
  • 修回日期:  2023-06-30
  • 网络出版日期:  2023-07-06
  • 刊出日期:  2024-04-24

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