基于卷积神经网络的天基预警雷达杂波抑制方法

段克清 李想 行坤 王永良

段克清, 李想, 行坤, 等. 基于卷积神经网络的天基预警雷达杂波抑制方法[J]. 雷达学报, 2022, 11(3): 386–398. doi: 10.12000/JR21161
引用本文: 段克清, 李想, 行坤, 等. 基于卷积神经网络的天基预警雷达杂波抑制方法[J]. 雷达学报, 2022, 11(3): 386–398. doi: 10.12000/JR21161
DUAN Keqing, LI Xiang, XING Kun, et al. Clutter mitigation in space-based early warning radar using a convolutional neural network[J]. Journal of Radars, 2022, 11(3): 386–398. doi: 10.12000/JR21161
Citation: DUAN Keqing, LI Xiang, XING Kun, et al. Clutter mitigation in space-based early warning radar using a convolutional neural network[J]. Journal of Radars, 2022, 11(3): 386–398. doi: 10.12000/JR21161

基于卷积神经网络的天基预警雷达杂波抑制方法

doi: 10.12000/JR21161
基金项目: 国家自然科学基金(61871397)
详细信息
    作者简介:

    段克清(1981–),男,河北石家庄人,2010年获国防科技大学信号与信息处理专业博士学位,现为中山大学电子与通信工程学院副教授。获国家技术发明二等奖和部委科技进步一等奖各1项,在IEEE Transaction on AES, IEEE Transaction on AC和中国科学等期刊与会议发表论文80余篇(SCI收录28篇)。IEEE会员,中国电子学会高级会员。主要研究方向包括空时自适应处理、机载/星载雷达信号处理和阵列信号处理等

    李 想(1997–),男,广东茂名人,现为中山大学电子与通信工程学院在读硕士生。主要研究方向包括阵列信号处理、空时自适应处理和深度学习等

    行 坤(1980–),男,陕西西安人,2006年获西安电子科技大学信号与信息处理专业硕士学位,现为中国科学院空天信息创新研究院副研究员,中国科学院大学硕士生导师。主要研究方向包括机载多功能雷达、地基监视雷达系统总体设计与性能仿真和动目标检测与跟踪技术等

    王永良(1965–),男,浙江嘉兴人,中国科学院院士,空军预警学院教授、博士生导师。主要研究方向包括空时自适应处理、雷达信号处理和阵列信号处理等

    通讯作者:

    王永良 ylwangkjld@163.com

  • 责任主编:廖桂生 Corresponding Editor: LIAO Guisheng
  • 中图分类号: TN957.51

Clutter Mitigation in Space-based Early Warning Radar Using a Convolutional Neural Network

Funds: The National Natural Science Foundation of China (61871397)
More Information
  • 摘要: 利用天基预警雷达实现动目标指示具有重要的军事应用价值。对于天基预警雷达,其平台高速运动及受地球自转影响导致杂波复杂非平稳性,更大的波束照射区域带来更严重的杂波非均匀性,从而导致适用于机载预警雷达的传统空时自适应处理(STAP)方法无法直接应用。针对上述问题,该文分析了天基预警雷达杂波分布特性,并构建了基于卷积神经网络(CNN)超分辨谱估计的STAP处理框架。首先,利用雷达系统和卫星轨道参数,仿真随机生成不同纬度、距离门、阵元误差、杂波起伏和地貌散射系数的回波数据集;然后,设计并调优了含5个权重层的二维CNN,实现由小样本所估低分辨杂波谱到高分辨谱的非线性映射;最后,基于高分辨空时谱构造空时滤波器实现杂波抑制和目标检测。仿真实验验证所提方法在小样本条件下可实现次最优杂波抑制性能,同时所需在线运算量远低于现有稀疏超分辨类方法,因此适用于天基预警雷达实际应用。

     

  • 图  1  天基预警雷达几何坐标系

    Figure  1.  Space-based early warning radar viewing geometry

    图  2  天基预警雷达杂波分布曲线

    Figure  2.  Clutter distribution of space-based early warning radar

    图  3  所提CNN模型结构

    Figure  3.  Structure of our CNN

    图  4  网络训练收敛性分析

    Figure  4.  Convergence analysis of our CNN

    图  5  高分辨空时谱对比结果

    Figure  5.  Comparison of high-resolution angle-Doppler spectra

    图  6  各方法信杂噪比损失曲线对比

    Figure  6.  Comparison of SCNR Loss curves

    图  7  相对平均SCNR Loss对比结果

    Figure  7.  Comparison of relative average SCNR Loss

    图  8  运算复杂度对比结果

    Figure  8.  Comparison of computational complexity

    表  1  仿真参数

    Table  1.   Parameters of simulation

    参数数值
    卫星轨道506 km
    卫星轨道倾角70o
    卫星速度7610 m/s
    天线尺寸50 m×2 m
    阵元数384×12
    合成通道数32×1
    工作频率1250 MHz
    带宽3 MHz
    主波束方位角90o
    主波束俯仰角–30o
    脉冲重复频率10000 Hz
    相参脉冲数16
    峰值辐射功率25 kW
    系统损耗8 dB
    下载: 导出CSV

    表  2  稳定性分析

    Table  2.   Stability analysis

    方法 ≤1 dB≤3 dB≤5 dB≤7 dB≤9 dB
    FOCUSS STAP5.4%35.0%70.0%87.0%94.8%
    SBL STAP44.0%85.2%90.0%97.8%98.0%
    CNN STAP32.2%73.6%84.8%92.0%96.3%
    下载: 导出CSV

    表  3  运算复杂度分析

    Table  3.   Analysis of computational complexity

    方法 运算复杂度
    FOCUSS STAP$\begin{aligned} & {O}\left(\left(NM{N}_{\mathrm{s} }{N}_{\mathrm{d} }+{\left(NM\right)}^{3}+2{\left(NM\right)}^{2}{N}_{\mathrm{s} }{N}_{\mathrm{d} }\right.\right.\\& \left.\left.+NM{\left({N}_{\mathrm{s} }{N}_{\mathrm{d} }\right)}^{2}\right){k}_{\mathrm{F}\mathrm{O}\mathrm{C} }\right)\end{aligned}$
    SBL STAP$\begin{aligned} & {O}\left(\left(NM{N}_{\mathrm{s} }{N}_{\mathrm{d} }+{\left(NM\right)}^{3}+3{\left(NM\right)}^{3}{N}_{\mathrm{s} }{N}_{\mathrm{d} }\right.\right.\\& \left.\left.+2NM{\left({N}_{\mathrm{s} }{N}_{\mathrm{d} }\right)}^{2}\right){k}_{\mathrm{S}\mathrm{B}\mathrm{L} }\right)\end{aligned}$
    CNN STAP${O}\left(28777{N}_{\mathrm{s} }{N}_{\mathrm{d} }\right)$
    下载: 导出CSV

    表  4  运行时间比较

    Table  4.   Comparison of running time

    方法离线时间(s)在线时间(s)
    FOCUSS STAP030.84
    SBL STAP033.02
    CNN STAP54810.013
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-10-30
  • 修回日期:  2021-12-18
  • 网络出版日期:  2022-01-11
  • 刊出日期:  2022-06-28

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