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面向复杂场景的多通道慢速动目标稳健检测算法

刘昆 贺雄鹏 廖桂生 余悦 王麒凯

刘昆, 贺雄鹏, 廖桂生, 余悦, 王麒凯. 面向复杂场景的多通道慢速动目标稳健检测算法[J]. 电子与信息学报. doi: 10.11999/JEIT230958
引用本文: 刘昆, 贺雄鹏, 廖桂生, 余悦, 王麒凯. 面向复杂场景的多通道慢速动目标稳健检测算法[J]. 电子与信息学报. doi: 10.11999/JEIT230958
LIU Kun, HE Xiongpeng, LIAO Guisheng, YU Yue, WANG Qikai. A Robust Multi-Channel Moving Target Detection Algorithm for Complex Scenes[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230958
Citation: LIU Kun, HE Xiongpeng, LIAO Guisheng, YU Yue, WANG Qikai. A Robust Multi-Channel Moving Target Detection Algorithm for Complex Scenes[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230958

面向复杂场景的多通道慢速动目标稳健检测算法

doi: 10.11999/JEIT230958
基金项目: 国家自然科学基金(62201408, 61931016),雷达信号处理国家级重点实验室支持计划项目(JKW202108)
详细信息
    作者简介:

    刘昆:男,博士生,研究方向为SAR/GMTI、高分宽幅成像等

    贺雄鹏:男,博士,副教授,研究方向为SAR、阵列信号处理等

    廖桂生:男,博士,教授,研究方向为阵列信号处理、新体制雷达等

    余悦:女,博士生,研究方向为波形分集体制雷达成像、动目标参数估计等

    王麒凯:男,硕士生,研究方向为波形分集体制雷达成像、地面运动目标检测等

    通讯作者:

    贺雄鹏 xphe@xidian.edu.cn

  • 中图分类号: TN957.51

A Robust Multi-Channel Moving Target Detection Algorithm for Complex Scenes

Funds: The National Nature Science Foundation of China (62201408, 61931016), The National Radar Signal Processing Laboratory (JKW202108)
  • 摘要: 针对鲁棒主成分分析(RPCA)算法在多通道慢速地面动目标指示(GMTI)中存在的高虚警以及对通道误差敏感问题,该文提出一种数据重构与速度合成孔径雷达(VSAR)-RPCA联合处理的方法。首先,通过样本挑选与联合像素法完成通道间数据精确重构;然后结合VSAR检测模式提出一种新的RPCA优化模型,通过采用交替投影乘子法对其进行求解得到空间频域的稀疏矩阵,进一步利用动目标与强杂波残余在空间频域通道的分布特性差异实现强杂波残余剔除与动目标检测;最后采用沿航迹干涉算法估计目标径向速度完成动目标重定位。相较于传统RPCA算法,所提算法在非理想强杂波背景下的虚警率显著降低。理论分析与实测实验验证了所提算法的有效性。
  • 图  1  多通道SAR系统对地观测几何

    图  2  本文方法处理流程

    图  3  联合像素构造示意图

    图  4  各通道SAR成像结果

    图  5  不同处理方法的干涉相位图

    图  6  动目标检测结果对比

    图  7  传统RPCA与提出方法迭代过程对比

    图  8  动目标检测、参数估计与重定位结果

    1  传统RPCA模型求解算法

     输入:观测矩阵$ {\boldsymbol{D}} $,参数$ \kappa $;
     输出:低秩矩阵$ {{\boldsymbol{A}}_{k + 1}} $,稀疏矩阵$ {{\boldsymbol{E}}_{k + 1}} $;
     初始化:$ {{\boldsymbol{Y}}_0} $=0,$ {\mu _0} \gt 0 $, $k = 0$, $\delta = 1{\boldsymbol{e}} - 6$, ${\mathrm{iter}} = 1000$;
     While ${\left\| {{\boldsymbol{D}} - {{\boldsymbol{A}}_{k + 1}} - {{\boldsymbol{E}}_{k + 1}}} \right\|_{\rm F}} > \delta $ or $k < {\text{iter}}$ do
      $ \left( {{\boldsymbol{U,\varXi ,V}}} \right) = {\rm{svd}}\left( {{\boldsymbol{D}} - {{\boldsymbol{E}}_k} + {{{{\boldsymbol{Y}}_k}} \mathord{\left/ {\vphantom {{{{\boldsymbol{Y}}_k}} \mu }} \right. } \mu }} \right) $;
      $ {{\boldsymbol{A}}_{k + 1}} = {\boldsymbol{U}}{{S}_{{1 \mathord{\left/ {\vphantom {1 \mu }} \right. } \mu }}}\left[ {\boldsymbol{\varXi }} \right]{{\boldsymbol{V}}^{\mathrm{H}}} $;
      $ {{\boldsymbol{E}}_{k + 1}} = {{S}_{{\kappa \mathord{\left/ {\vphantom {\kappa \mu }} \right. } \mu }}}\left( {{\boldsymbol{D}} - {{\boldsymbol{A}}_{k + 1}} + {{{{\boldsymbol{Y}}_k}} \mathord{\left/ {\vphantom {{{{\boldsymbol{Y}}_k}} \mu }} \right. } \mu }} \right) $;
      $ {{\boldsymbol{Y}}_{k + 1}} = {{\boldsymbol{Y}}_k} + \mu \left( {{\boldsymbol{D - }}{{\boldsymbol{A}}_{k + 1}}{\boldsymbol{ - }}{{\boldsymbol{E}}_{k + 1}}} \right) $;
      $k = k + 1$;
     End while
    下载: 导出CSV

    2  所提RPCA优化模型求解流程

     输入:观测矩阵$ {\boldsymbol{D}} $,参数$ \lambda $;
     输出:低秩矩阵$ {{\boldsymbol{A}}_{k + 1}} $,稀疏矩阵$ {F}{\left( {\boldsymbol{E}} \right)_{k + 1}} $;
     初始化:$ {{\boldsymbol{Y}}_0} $=0; $ {\mu _0} \gt 0 $, $k = 0$, $\delta = 1{\boldsymbol{e}} - 6$, ${\mathrm{iter}} = 1000$;
     While ${\left\| {{\boldsymbol{D}} - {{\boldsymbol{A}}_{k + 1}} - {{\boldsymbol{E}}_{k + 1}}} \right\|_{\rm F}} > \delta $ or $k < {\mathrm{iter}}$ do
      $ \left( {{\boldsymbol{U,\varSigma ,V}}} \right) = {\rm{svd}}\left( {{\boldsymbol{D}} - {{\boldsymbol{E}}_k} + {{{{\boldsymbol{Y}}_k}} \mathord{\left/ {\vphantom {{{{\boldsymbol{Y}}_k}} \mu }} \right. } \mu }} \right) $;
      $ {{\boldsymbol{A}}_{k + 1}} = {\boldsymbol{U}}{S_{{1 \mathord{\left/ {\vphantom {1 \mu }} \right. } \mu }}}\left[ {\boldsymbol{\varXi }} \right]{{\boldsymbol{V}}^H} $;
      $ {F}{\left( {\boldsymbol{E}} \right)_{k + 1}} = {S_{{\kappa \mathord{\left/ {\vphantom {\kappa \mu }} \right. } \mu }}}\left( {{F}\left( {{\boldsymbol{D}} - {{\boldsymbol{A}}_{k + 1}} + {{{{\boldsymbol{Y}}_k}} \mathord{\left/ {\vphantom {{{{\boldsymbol{Y}}_k}} \mu }} \right. } \mu }} \right)} \right) $;
      ${{\boldsymbol{E}}_{k + 1}} = {{F}^{ - 1}}\left( {{F}{{\left( {\boldsymbol{E}} \right)}_{k + 1}}} \right)$;
      $ {{\boldsymbol{Y}}_{k + 1}} = {{\boldsymbol{Y}}_k} + \mu \left( {{\boldsymbol{D}} - {{\boldsymbol{A}}_{k + 1}} - {{\boldsymbol{E}}_{k + 1}}} \right) $;
      $k = k + 1$;
     End while
    下载: 导出CSV

    表  1  多通道SAR系统参数

    参数
    载频(GHz)8.85
    带宽(MHz)40
    采样频率(MHz)60
    脉冲重复频率(Hz)1000
    通道间距(m)0.559
    平台速度(m/s)115
    通道数目(个)3
    下载: 导出CSV

    表  2  不同处理方法通道相关性

    方法相关性
    文献[31]算法0.80
    数据重构0.90
    本文算法0.94
    下载: 导出CSV

    表  3  不同目标检测算法耗时对比

    方法耗时(s)
    DPCA0.007
    RPCA4.970
    VSAR-RPCA5.300
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-09-04
  • 修回日期:  2024-05-06
  • 网络出版日期:  2024-05-12

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