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基于历史运动特征约束和SVM频谱分类的被动声呐目标关联跟踪方法

钱宇宁 陈亚伟 李归

钱宇宁, 陈亚伟, 李归. 基于历史运动特征约束和SVM频谱分类的被动声呐目标关联跟踪方法[J]. 电子与信息学报, 2023, 45(8): 2991-3001. doi: 10.11999/JEIT220895
引用本文: 钱宇宁, 陈亚伟, 李归. 基于历史运动特征约束和SVM频谱分类的被动声呐目标关联跟踪方法[J]. 电子与信息学报, 2023, 45(8): 2991-3001. doi: 10.11999/JEIT220895
QIAN Yuning, CHEN Yawei, LI Gui. Target Association and Tracking Approach Based on Historical Kinematic Characteristics and SVM Spectrum Classification for Passive Sonar[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2991-3001. doi: 10.11999/JEIT220895
Citation: QIAN Yuning, CHEN Yawei, LI Gui. Target Association and Tracking Approach Based on Historical Kinematic Characteristics and SVM Spectrum Classification for Passive Sonar[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2991-3001. doi: 10.11999/JEIT220895

基于历史运动特征约束和SVM频谱分类的被动声呐目标关联跟踪方法

doi: 10.11999/JEIT220895
详细信息
    作者简介:

    钱宇宁:男,博士,高级工程师,研究方向为信号处理

    通讯作者:

    钱宇宁 inter101010@sina.com

  • 中图分类号: TN911.72

Target Association and Tracking Approach Based on Historical Kinematic Characteristics and SVM Spectrum Classification for Passive Sonar

  • 摘要: 针对航迹交叉条件下被动声呐目标跟踪困难的问题,该文将现有运动特征关联方法和信号特征辅助关联方法进行改进融合,提出一种基于历史运动特征约束和支持向量机(His-SVM)频谱分类的被动声呐目标关联跟踪方法。首先,利用目标的历史航迹点提取历史方位变化率,作为重合条件下点航迹关联的主要特征;其次,将方位靠近目标的点迹关联问题转化为点迹频谱的分类问题,利用目标航迹点频谱训练的SVM模型完成待关联点迹频谱的分类,根据分类结果实现方位靠近目标的点航迹关联;最后,将两种方法有机融合,构建了被动声呐交叉重合目标关联跟踪的算法框架。仿真实验结果表明,该算法能够有效完成靠近目标的点迹分类和交叉重合目标的关联跟踪,其跟踪性能优于传统运动特征关联跟踪算法。
  • 图  1  被动声呐交叉目标示例

    图  2  历史运动特征关联算法步骤

    图  3  SVM频谱分类的关联算法步骤

    图  4  基于历史运动特征约束和SVM频谱分类的关联跟踪算法框架

    图  5  待关联量测集合示意图

    图  6  仿真目标时间方位历程图

    图  7  仿真4目标频谱

    图  8  仿真4目标交叉时间方位历程图

    图  9  His-SVM跟踪结果

    图  10  Tra跟踪结果

    图  11  His跟踪结果

    图  12  Tra-SVM跟踪结果

    图  13  MHT跟踪结果

    表  1  SVM与相关系数方法分类正确率(%)比较

    目标1、2分类目标2、3分类目标3、4分类目标1、2分类
    (线谱漂移)
    目标2、3分类
    (线谱漂移)
    目标1目标2目标2目标3目标3目标4目标1目标2目标2目标3
    SVM(60 s训练)10010010010099.58100100100100100
    SVM(30 s训练)10010010010098.8910097.4197.41100100
    相关系数10010010089.5898.7547.507577.9274.5890.83
    下载: 导出CSV

    表  2  5种算法处理性能比较

    His-SVMTraHisTra-SVMMHT
    跟踪误差(°)0.182.930.322.480.33
    处理时间(s)0.0150.0090.0090.0110.019
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-07-04
  • 修回日期:  2022-12-02
  • 录用日期:  2022-12-20
  • 网络出版日期:  2022-12-23
  • 刊出日期:  2023-08-21

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