基于区域特征细化感知学习的星载SAR图像有源压制干扰抑制方法

聂林 韦顺军 李佳慧 张浩 师君 王谋 陈思远 张鑫焱

聂林, 韦顺军, 李佳慧, 等. 基于区域特征细化感知学习的星载SAR图像有源压制干扰抑制方法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24072
引用本文: 聂林, 韦顺军, 李佳慧, 等. 基于区域特征细化感知学习的星载SAR图像有源压制干扰抑制方法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24072
NIE Lin, WEI Shunjun, LI Jiahui, et al. Active blanket jamming suppression method for spaceborne SAR images based on regional feature refinement perceptual learning[J]. Journal of Radars, in press. doi: 10.12000/JR24072
Citation: NIE Lin, WEI Shunjun, LI Jiahui, et al. Active blanket jamming suppression method for spaceborne SAR images based on regional feature refinement perceptual learning[J]. Journal of Radars, in press. doi: 10.12000/JR24072

基于区域特征细化感知学习的星载SAR图像有源压制干扰抑制方法

doi: 10.12000/JR24072
基金项目: 国家自然科学基金(62271108)
详细信息
    作者简介:

    聂  林,硕士生,主要研究方向为SAR干扰及抗干扰、雷达信号处理、机器学习等

    韦顺军,博士,教授,主要研究方向为雷达三维成像、新体制SAR成像、雷达稀释成像、雷达信号处理、SAR系统应用等

    李佳慧,硕士生,主要研究方向为SAR图像生成、雷达三维成像、雷达信号处理等

    张 浩,硕士,主要研究方向为雷达信号处理、雷达干扰抑制、机器学习等

    师  君,博士,副教授,主要研究方向为雷达信号处理、SAR成像系统、SAR图像智能解译等

    王 谋,博士,主要研究方向为雷达三维成像、雷达稀释成像、雷达信号处理、SAR系统应用、机器学习等

    陈思远,硕士,工程师,主要研究方向为雷达信号处理等

    张鑫焱,硕士,高级工程师,主要研究方向为雷达系统仿真技术等

    通讯作者:

    韦顺军 weishunjun@uestc.edu.cn

  • 责任主编:陈思伟 Corresponding Editor: CHEN Siwei
  • 中图分类号: TN974

Active Blanket Jamming Suppression Method for Spaceborne SAR Images Based on Regional Feature Refinement Perceptual Learning

Funds: The National Natural Science Foundation of China (62271108)
More Information
  • 摘要: 星载合成孔径雷达(SAR)系统常受到强电磁干扰而导致成像质量下降,但现有基于图像域的干扰抑制方法易造成图像失真、纹理细节信息丢失等难题。针对上述问题,该文提出了一种基于区域特征细化感知学习的星载SAR图像有源压制干扰抑制方法。首先,建立了星载SAR图像域有源压制干扰信号和图像模型;其次,设计一种基于区域特征感知的高精度干扰识别网络,利用高效通道注意力机制,提取SAR图像有源压制干扰图样特征,可以有效识别SAR图像干扰区域;然后,构建一种基于SAR图像和压制干扰特征联合学习的多元区域特征细化干扰抑制网络,将SAR图像切分为多元区域,采用多模块协同处理多元区域上的压制干扰特征,实现复杂场景条件下SAR图像有源压制干扰的精细化抑制。最后,构建SAR图像有源压制干扰仿真数据集,且采用哨兵1号实测数据进行实验验证分析。实验结果表明所提方法能有效识别和抑制星载SAR图像多种典型有源压制干扰。

     

  • 图  1  星载SAR压制干扰几何模型

    Figure  1.  Spaceborne SAR suppression jamming geometry

    图  2  干扰抑制方法流程图

    Figure  2.  Flowchart of the proposed interference suppression method

    图  3  IRRFPNet结构示意图

    Figure  3.  The structure of the proposed IRRFPNet

    图  4  DRFRISNet结构示意图

    Figure  4.  The structure of the proposed DRFRISNet

    图  5  低层别特征提取单元

    Figure  5.  The structure of LLFE-module

    图  6  多尺度平衡-特征聚焦处理单元

    Figure  6.  The schematic diagram of MEFFP-module

    图  7  ALSA单元及PPFRR单元结构示意图

    Figure  7.  The structure of ALSA-module and PPFRR-module

    图  8  仿真数据对应区域

    Figure  8.  Simulation data correspondence area

    图  9  5种网络训练过程损失值曲线和总体识别精度对比

    Figure  9.  Comparison of loss value and mean overall accuracy of five networks in training process

    图  10  干扰抑制仿真实验结果

    Figure  10.  Results of interference suppression simulation experiments

    图  11  图10场景1、场景2中的干扰区域放大图

    Figure  11.  Enlarged view of the interference region of Fig. 10 scene 1 and scene 2

    图  12  实测数据对应的大场景区域(红色框表示成像区域)

    Figure  12.  Large scenario regions corresponding to measured data (where the red box indicates the imaging area)

    图  13  实测数据中干扰切片的时频谱图

    Figure  13.  Time-frequency spectra of interference slices in the measured data

    图  14  哨兵1号数据的干扰抑制实测实验结果

    Figure  14.  Experimental results of interference suppression measurement experiments on Sentinel-1 data

    图  15  图14场景7、图14场景8场景中的干扰区域放大图

    Figure  15.  Enlarged view of the interference region in scene 7 and scene 8 in Fig. 14

    图  16  实测数据上LLFE单元的消融实验结果

    Figure  16.  Ablation experiments on LLFE with real data

    表  1  干扰参数设置

    Table  1.   Jamming parameters configuration

    参数 移频干扰及噪声卷积调制干扰 射频噪声干扰
    中心频率 4 GHz 10 GHz
    采样率 120 MHz 120 MHz
    脉冲持续时间 3 μs 10 μs
    调频斜率 1.6655$ \times $1013 Hz/s 5$ \times $1013 Hz/s
    脉冲重复频率 1000 Hz 1000 Hz
    场景中心最近斜距 1136 m 8485 m
    下载: 导出CSV

    表  2  5种网络识别性能比较(%)

    Table  2.   Comparison of recognition performance of five networks (%)

    测试集 方法 射频噪声干扰 卷积调制干扰 移频干扰 无干扰 OA


    Test_1
    SpnasNet100 95.208 88.125 93.375 94.750 92.865
    FBNetc100 97.750 83.750 94.620 97.083 93.301
    ResNet18 82.604 73.750 80.067 81.625 79.512
    MnasNet 96.250 92.500 95.625 97.060 95.359
    IRRFPNet 99.806 99.628 99.760 99.905 99.775
    Test_2 SpnasNet100 95.833 78.625 89.500 91.792 88.938
    FBNetc100 96.235 84.375 94.125 93.958 92.173
    ResNet18 81.460 72.165 77.250 82.500 78.344
    MnasNet 98.750 91.875 96.250 95.625 96.625
    IRRFPNet 99.875 99.792 99.840 99.925 99.858


    Test_3
    SpnasNet100 92.315 82.583 96.250 89.255 90.101
    FBNetc100 95.000 85.250 90.402 94.750 91.351
    ResNet18 80.572 74.500 81.267 79.875 79.054
    MnasNet 97.372 85.625 98.125 96.958 94.520
    IRRFPNet 99.790 99.895 99.370 100.00 99.764


    Test_4
    SpnasNet100 94.875 81.015 92.000 92.583 90.118
    FBNetc100 93.750 80.625 91.535 95.000 90.228
    ResNet18 79.708 72.250 78.375 80.917 77.813
    MnasNet 98.125 87.500 97.500 97.002 95.032
    IRRFPNet 99.885 99.680 99.792 99.950 99.827
    下载: 导出CSV

    表  3  不同方法仿真实验的IEN, ISH及ICO指标计算结果

    Table  3.   Calculation of IEN, ISH and ICO for simulation experiments with different methods

    场景(图10) 方法 IEN ISH ICO
    场景1 真值图像 5.2194 –17.6613 3.0279
    陷波滤波方法 5.3487 –19.6172 4.2246
    RESCAN 5.2284 –14.7195 1.5356
    DRDNet 5.2154 –17.6395 3.0118
    DRFRISNet 5.2195 –17.6701 3.0127
    场景2 真值图像 5.9682 –15.2466 1.7486
    陷波滤波方法 5.4960 –19.9296 3.5259
    RESCAN 5.9116 –14.1725 1.3188
    DRDNet 5.9688 –15.2465 1.7455
    DRFRISNet 5.9678 –15.2465 1.7480
    场景3 真值图像 6.9088 –9.6839 1.1570
    陷波滤波方法 6.6281 –13.7598 1.7738
    RESCAN 7.1879 –8.9641 1.0095
    DRDNet 7.0350 –9.3704 1.2169
    DRFRISNet 6.8098 –9.7194 1.1240
    场景4 真值图像 6.9806 –10.5063 1.6968
    陷波滤波方法 6.9089 –12.4531 1.6764
    RESCAN 7.0970 –8.9138 1.2965
    DRDNet 6.9258 –10.1447 1.6503
    DRFRISNet 6.9495 –10.2587 1.6625
    下载: 导出CSV

    表  4  不同方法仿真实验的PSNR及SSIM指标计算结果

    Table  4.   Calculation of PSNR and SSIM for simulation experiments with different methods

    场景(图10) 陷波滤波方法 RESCAN DRDNet DRFRISNet
    PSNR (dB) SSIM PSNR (dB) SSIM PSNR (dB) SSIM PSNR (dB) SSIM
    场景1 16.3777 0.0386 27.7221 0.8847 31.2742 0.9459 37.9279 0.9889
    场景2 16.2330 0.4063 29.2225 0.8790 33.7739 0.9582 38.4685 0.9905
    场景3 13.8495 0.3880 26.3501 0.8551 30.8176 0.9351 33.5751 0.9818
    场景4 15.0873 0.5388 26.6142 0.8661 31.9454 0.9405 34.2852 0.9872
    下载: 导出CSV

    表  5  不同方法实测实验的IEN, ISH及ICO指标计算结果

    Table  5.   Calculation of IEN, ISH and ICO for simulation experiments with different methods

    场景(图14) 方法 IEN ISH ICO 场景(图14) 方法 IEN ISH ICO
    场景5 真值图像 4.2833 –15.9504 4.8074 场景8 真值图像 6.4156 –12.9350 1.6968
    陷波滤波方法 5.4302 –16.9256 3.9217 陷波滤波方法 5.2377 –22.1863 1.6764
    RESCAN 2.0471 –16.5179 4.7079 RESCAN 5.8306 –14.1303 1.2965
    DRDNet 2.1126 –16.3571 4.9755 DRDNet 5.8738 –14.5922 1.6503
    DRFRISNet 4.5146 –16.0021 4.8075 DRFRISNet 5.8820 –12.8447 1.6625
    场景6 真值图像 7.6337 –6.8687 1.7486 场景9 真值图像 4.7258 –13.1804 1.1027
    陷波滤波方法 6.9500 –12.0783 3.5259 陷波滤波方法 5.7357 –18.7759 1.2968
    RESCAN 7.5334 –7.1883 1.3188 RESCAN 3.0881 –16.2433 0.9492
    DRDNet 7.5670 –6.7022 1.7455 DRDNet 5.3006 –13.4509 1.0238
    DRFRISNet 7.6074 –6.9875 1.7480 DRFRISNet 4.3446 –13.0803 1.0785
    场景7 真值图像 6.6716 –10.5940 1.1570 场景10 真值图像 7.0936 –9.2369 1.0057
    陷波滤波方法 5.8949 –18.3770 1.7738 陷波滤波方法 6.1757 –16.8893 1.8313
    RESCAN 6.3696 –9.1933 1.0095 RESCAN 6.6451 –11.9947 0.9725
    DRDNet 6.3970 –9.5748 1.2169 DRDNet 6.6612 –10.5871 1.0673
    DRFRISNet 6.4196 –10.5294 1.1240 DRFRISNet 6.8497 –10.0975 1.0162
    下载: 导出CSV

    表  6  不同方法实测实验的PSNR及SSIM指标计算结果

    Table  6.   Calculation of PSNR and SSIM for simulation experiments with different methods

    场景(图14) 干扰图像 陷波滤波方法 RESCAN DRDNet DRFRISNet
    PSNR (dB) SSIM PSNR (dB) SSIM PSNR (dB) SSIM PSNR (dB) SSIM PSNR (dB) SSIM
    场景5 30.4133 0.9762 23.7897 0.8769 29.8113 0.9709 30.4442 0.9777 33.8496 0.9895
    场景6 17.0048 0.8226 10.8052 0.2450 19.7852 0.8897 22.0794 0.9336 24.2911 0.9615
    场景7 17.4517 0.5136 11.2595 0.1435 23.8517 0.7756 24.4211 0.8170 25.1023 0.8409
    场景8 20.6213 0.6646 14.5254 0.1686 22.9472 0.7413 24.4636 0.7874 26.4066 0.9075
    场景9 20.1741 0.8653 15.3698 0.3536 22.1510 0.8895 30.3326 0.9860 31.6360 0.9910
    场景10 16.5528 0.5407 11.5139 0.1652 18.8683 0.6463 20.3926 0.7298 30.9412 0.9693
    下载: 导出CSV

    表  7  对LLFE单元进行消融实验的PSNR及SSIM指标计算结果

    Table  7.   Calculation of PSNR and SSIM metrics for ablation experiments on LLFE-Moudle

    场景(图16) 无LLFE单元 有LLFE单元
    PSNR (dB) SSIM PSNR (dB) SSIM
    场景11 23.7820 0.8988 30.5310 0.9815
    场景12 19.0430 0.8691 24.1102 0.9586
    场景13 24.3205 0.7932 25.2305 0.8490
    场景14 21.5722 0.7218 26.7804 0.9118
    下载: 导出CSV
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  • 收稿日期:  2024-04-25
  • 修回日期:  2024-06-07
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