纹理异常感知SAR自监督学习干扰抑制方法

韩朝赟 岑熙 崔嘉禾 李亚超 张鹏

韩朝赟, 岑熙, 崔嘉禾, 等. 纹理异常感知SAR自监督学习干扰抑制方法[J]. 雷达学报, 2023, 12(1): 154–172. doi: 10.12000/JR22168
引用本文: 韩朝赟, 岑熙, 崔嘉禾, 等. 纹理异常感知SAR自监督学习干扰抑制方法[J]. 雷达学报, 2023, 12(1): 154–172. doi: 10.12000/JR22168
HAN Zhaoyun, CEN Xi, CUI Jiahe, et al. Self-supervised learning method for SAR interference suppression based on abnormal texture perception[J]. Journal of Radars, 2023, 12(1): 154–172. doi: 10.12000/JR22168
Citation: HAN Zhaoyun, CEN Xi, CUI Jiahe, et al. Self-supervised learning method for SAR interference suppression based on abnormal texture perception[J]. Journal of Radars, 2023, 12(1): 154–172. doi: 10.12000/JR22168

纹理异常感知SAR自监督学习干扰抑制方法

doi: 10.12000/JR22168
基金项目: 国家重点研发计划(2018YFB2202500),国家自然科学基金 (62171337, 62101396),陕西省重点研发计划 (2017KW-ZD-12),陕西省杰出青年科学基金 (S2020-JC-JQ-0056),中央高校基本科研业务费专项资金(XJS212205)
详细信息
    作者简介:

    韩朝赟,硕士生,主要研究方向为深度学习、雷达图像目标检测识别、雷达抗干扰等

    岑 熙,博士生,主要研究方向为复杂电磁环境对抗、图像处理及深度学习等

    崔嘉禾,硕士,主要研究方向为SAR图像处理、卫星通信

    李亚超,博士,教授,博士生导师,主要研究方向为SAR/ISAR成像、弹载SAR成像、地面运动目标检测、基于FPGA和DSP的实时信号处理、分布式雷达等

    张 鹏,博士,副教授,博士生导师,主要研究方向为雷达抗干扰、雷达图像处理与分析、统计学习理论等

    通讯作者:

    李亚超 ycli@mail.xidian.edu.cn

  • 责任主编:张磊 Corresponding Editor: ZHANG Lei
  • 中图分类号: TN974

Self-supervised Learning Method for SAR Interference Suppression Based on Abnormal Texture Perception

Funds: The National Key R&D Program of China (2018YFB2202500), The National Natural Science Foundation of China (62171337, 62101396), The Key R&D Program of Shaanxi Province (2017KW-ZD-12), Shaanxi Province Funds for Distinguished Young Youths (S2020-JC-JQ-0056), Fundamental Research Funds for the Central Universities (XJS212205)
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  • 摘要: 面对日渐复杂的电磁干扰环境,合成孔径雷达干扰抑制已成为亟须解决的难题。现有主流合成孔径雷达非参数/参数化干扰抑制方法,严重依赖干扰先验和强能量差异,存在计算复杂度高、信号损失严重等问题,难以满足对抗日益复杂的干扰的需求。针对上述问题,该文提出一种基于纹理异常感知的SAR自监督学习干扰抑制方法,利用正常雷达回波与干扰的时频域纹理差异性特征克服干扰先验的约束。首先,构建了一种干扰时频定位网络模型Location-Net,对雷达回波时频谱进行压缩重构,根据网络的重构误差对干扰进行时频定位;其次,针对干扰抑制损失问题,构建了一种信号修复神经网络模型Recovery-Net,实现对干扰抑制后回波信号损失修复。相比传统方法,所提方法克服对干扰先验的需求,可有效对抗多种复杂干扰类型,具备较强的泛化能力。基于仿真和实测数据的抗干扰处理结果,验证了所提方法对多种有源主瓣压制干扰的有效性,并通过与3种现有抗干扰方法进行对比,体现了该算法的优越性。最后,对比了所提神经网络与主流轻量化神经网络的复杂度差异,结果表明设计的两个神经网络计算复杂度更低,具备实时应用前景。

     

  • 图  1  间歇采样转发/非均匀间歇采样转发/线性函数移频干扰的多域特性图

    Figure  1.  Multi-domain of interrupted-sampling and repeater jamming/heterogeneous interrupted-sampling and repeater jamming/linear function frequency shift jamming

    图  2  干扰时频定位网络模型(Location-Net)

    Figure  2.  Interference time-frequency location network model (Location-Net)

    图  3  信号修复网络模型(Recovery-Net)

    Figure  3.  Signal recovery network model (Recovery-Net)

    图  4  基于自监督学习的干扰抑制流程

    Figure  4.  Interference suppression process based on self-supervised learning

    图  5  本文方法处理各阶段时频域

    Figure  5.  Time-frequency of echo stage during processing by our method

    图  6  抗干扰前后雷达回波对比

    Figure  6.  Comparison of radar echo before and after anti-jamming

    图  7  信号修复后雷达回波相位误差

    Figure  7.  Phase error in radar echo after signal recovering

    图  8  现有方法抗干扰处理后回波时频域

    Figure  8.  Time-frequency of echo stage after processing by existing anti-jamming method

    图  9  仿真干扰数据抗干扰成像结果对比

    Figure  9.  Comparison of imaging result of data with simulated interference after anti-jamming

    图  10  实测相参压制干扰抗干扰时频域

    Figure  10.  Time-frequency spectrum of measured data with coherent suppression jamming after anti-jamming

    图  11  实测相参压制干扰抗干扰成像结果

    Figure  11.  Imaging result of measured data with coherent suppression jamming after anti-jamming

    图  12  实测相参运动多假目标干扰抗干扰时频域

    Figure  12.  Time-frequency spectrum of measured data with coherent motion and multi-false target interference after anti-jamming

    图  13  实测相参运动假目标干扰抗干扰成像结果

    Figure  13.  Imaging result of measured data with coherent motion and multi-false target interference after anti-jamming

    图  14  实测复合干扰抗干扰处理时频域

    Figure  14.  Time-frequency spectrum of measured data with complex interference after anti-jamming

    图  15  实测组合式干扰抗干扰处理成像结果

    Figure  15.  Imaging result of measured data with complex interference after anti-jamming

    表  1  干扰时频定位网络参数

    Table  1.   Interference time-frequency location network parameter list

    类型核/步长补零个数BN/激活
    卷积3×3×16/21是/ReLU
    卷积3×3×32/21是/ReLU
    转置卷积3×3×16/21是/ReLU
    转置卷积3×3×2/21
    下载: 导出CSV

    表  2  基于卷积层的信号修复网络参数

    Table  2.   Parameter list of signal recovery network based on convolution layers

    核/步长补零个数BN/激活
    5×5×8/(2,1)0是/ReLU
    5×5×16/(2,1)0是/ReLU
    5×5×24/10是/ReLU
    1×7×32/10是/ReLU
    1×7×32/10是/ReLU
    1×7×32/10是/ReLU
    1×1×2/10
    下载: 导出CSV

    表  3  加入仿真干扰的实测SAR回波参数

    Table  3.   Parameters of measured SAR echo with simulated jamming

    参数数值
    载频Ku波段
    带宽50 MHz
    采样频率60 MHz
    脉冲重复频率800 Hz
    平台运动速度80 m/s
    下载: 导出CSV

    表  4  仿真干扰数据抗干扰评估

    Table  4.   Anti-jamming evaluation of data with simulate interference

    方法ISR (dB)SDR (dB)SSIM
    IALM21.402.760.31
    ESP 5.720.070.05
    陷波滤波器16.705.440.21
    干扰抑制18.9511.180.77
    信号修复18.6513.560.94
    下载: 导出CSV

    表  5  实测数据雷达参数

    Table  5.   Radar parameters of measured data

    参数数值
    载频Ku波段
    带宽100 MHz
    采样频率120 MHz
    脉冲重复频率600 Hz
    平台速度85 m/s
    下载: 导出CSV

    表  6  实测相参压制干扰数据抗干扰评估

    Table  6.   Anti-jamming evaluation of measured data with coherent suppression jamming

    方法ISR (dB)MNR (dB)
    IALM14.72–7.62
    ESP 8.45–8.17
    陷波滤波器13.33–7.91
    本文方法18.72–8.43
    下载: 导出CSV

    表  7  实测相参运动假目标干扰数据抗干扰评估

    Table  7.   Anti-jamming evaluation of measured data with coherent motion and multi-false target interference

    方法ISR (dB)MNR (dB)
    IALM17.79–10.83
    ESP15.09–11.80
    陷波滤波器16.96 –9.58
    本文方法19.93 –7.85
    下载: 导出CSV

    表  8  实测组合式干扰数据抗干扰评估

    Table  8.   Anti-jamming evaluation of measured data with complex interference

    方法ISR (dB)MNR (dB)
    IALM16.61–12.41
    ESP14.48–7.34
    陷波滤波器17.21–8.21
    本文方法18.65–8.43
    下载: 导出CSV

    表  9  神经网络模型复杂度对比

    Table  9.   Comparison of neural network complexity

    模型参数量浮点运算量内存访问量
    MobileNet V23.5 M209.6 M110.6 M
    ShuffleNet V22.3 M98.7 M36.7 M
    IDN + IMN136.6 M53.8 G2.1 G
    L-Net + R-Net4333076.0 M14.1 M
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-09
  • 修回日期:  2022-10-10
  • 网络出版日期:  2022-10-21
  • 刊出日期:  2023-02-28

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