多域浅层特征引导下雷达有源干扰多模态对比识别方法

郭文杰 吴振华 曹宜策 张强 张磊 杨利霞

郭文杰, 吴振华, 曹宜策, 等. 多域浅层特征引导下雷达有源干扰多模态对比识别方法[J]. 雷达学报(中英文), 2024, 13(5): 1004–1018. doi: 10.12000/JR24129
引用本文: 郭文杰, 吴振华, 曹宜策, 等. 多域浅层特征引导下雷达有源干扰多模态对比识别方法[J]. 雷达学报(中英文), 2024, 13(5): 1004–1018. doi: 10.12000/JR24129
GUO Wenjie, WU Zhenhua, CAO Yice, et al. Multidomain characteristic-guided multimodal contrastive recognition method for active radar jamming[J]. Journal of Radars, 2024, 13(5): 1004–1018. doi: 10.12000/JR24129
Citation: GUO Wenjie, WU Zhenhua, CAO Yice, et al. Multidomain characteristic-guided multimodal contrastive recognition method for active radar jamming[J]. Journal of Radars, 2024, 13(5): 1004–1018. doi: 10.12000/JR24129

多域浅层特征引导下雷达有源干扰多模态对比识别方法

DOI: 10.12000/JR24129 CSTR: 32380.14.JR24129
基金项目: 国家自然科学基金(62201007, 62401007),中国博士后科学基金(2020M681992),安徽省自然科学基金(2308085QF199)
详细信息
    作者简介:

    郭文杰,硕士生,主要研究方向为雷达干扰信号智能识别

    吴振华,博士,副教授,主要研究方向为雷达智能干扰对抗

    曹宜策,博士,讲师,主要研究方向为雷达智能干扰对抗、遥感图像解译

    张 强,博士,研究员,主要研究方向为天基信号处理

    张 磊,博士,教授,主要研究方向为雷达信号处理

    杨利霞,博士,教授,主要研究方向为电磁散射与逆散射、电波传播及天线理论与设计、计算电磁学等

    通讯作者:

    吴振华 zhwu@ahu.edu.cn

    曹宜策 yccao@ahu.edu.cn

  • 责任主编:刘振 Corresponding Editor: LIU Zhen
  • 中图分类号: TN958

Multidomain Characteristic-guided Multimodal Contrastive Recognition Method for Active Radar Jamming

Funds: The National Natural Science Foundation of China (62201007, 62401007), The China Postdoctoral Science Foundation (2020M681992), Anhui Provincial Natural Science Foundation (2308085QF199)
More Information
  • 摘要: 在雷达有源干扰识别任务中,如何实现多域浅层特征与时频域深层网络特征的稳健联合,并在极端小样本下维持高干扰识别准确率是亟待解决的关键问题。针对此问题,该文提出一种多域浅层特征引导下雷达有源干扰多模态对比识别方法。在充分提取有源干扰多域浅层特征基础上,设计优选单元自动选择有效特征,生成对应含有隐式专家知识的文本模态。将文本模态与时频变换图像分别输入文本和图像编码器,构建多模态特征对并映射至模态对齐高维空间中,利用文本特征作为锚点,通过对比学习引导同类干扰的时频图像特征聚合,以优化图像编码器表征能力,实现干扰识别特征类内更聚集、类间更分离。实验结果表明,相较于已有深浅特征直接联合,所提引导式联合方法可以实现特征差异处理,从而提高识别特征判别力和泛化力。且在极端小样本条件(每类干扰训练样本为2~3个)下,所提识别方法较先进对比方法的准确率提升9.84%,证明了该文方法的有效性与鲁棒性。

     

  • 图  1  雷达发射信号与雷达干扰的时频图像

    Figure  1.  Time-frequency spectrograms of the transmission signal and radar jamming

    图  2  所提方法的算法流程图

    Figure  2.  Algorithm flowchart of the proposed method

    图  3  多域特征优选单元算法流程示意图(以NCJ为例)

    Figure  3.  Algorithm flowchart of the multidomain characteristics selection unit (taking NCJ as an example)

    图  4  时频图像深层特征的降维可视化分布图

    Figure  4.  Visualization of time-frequency images deep features

    图  5  本文方法和对比方法的混淆矩阵实验结果

    Figure  5.  Confusion matrix experimental results between the proposed method and the comparative methods

    图  6  不同方法在不同训练样本下的识别准确率折线图

    Figure  6.  Recognition accuracy line chart of different methods under different numbers of training samples

    表  1  雷达干扰多域浅层特征

    Table  1.   Multidomain characteristics of radar jamming

    调制域 干扰特征中英文名称
    时域 矩偏度
    Time domain skewness
    矩峰度
    Time domain kurtosis
    包络起伏度
    Envelope undulation
    快速脉内调制识别参数
    Fast intra pulse modulation recognition parameters
    频域 矩偏度
    Frequency domain skewness
    矩峰度
    Frequency domain kurtosis
    载波因子
    Carrier factor
    加性高斯白噪声因子
    Additive Gaussian white noise factor
    时频域 时频图像重心
    Centroid of time-frequency image
    时频图像标准差
    Time-frequency image standard deviation
    双谱域 双谱方差
    Bispectral variance
    双谱均值
    Bispectral mean
    小波域 方差
    Variance
    均值
    Mean value
    最大值
    Maximum
    尺度重心
    Scale center of gravity
    最大奇异值
    Maximum singular value
    中心矩特征
    Central moment feature
    统计域 信息熵
    Information entropy
    指数熵
    Exponential entropy
    范数熵
    Norm entropy
    下载: 导出CSV

    表  2  EfficientNetV2网络结构

    Table  2.   EffcientNetV2 architecture

    阶段名称通道数层数状态
    0Conv241冻结
    1Fused-MBConv242冻结
    2Fused-MBConv484冻结
    3Fused-MBConv644冻结
    4MBConv1286冻结
    5MBConv1609训练
    6MBConv27215训练
    7Conv & Pooling & FC17921训练
    下载: 导出CSV

    表  3  雷达发射信号与干扰参数设置

    Table  3.   Parameter setting of transmission signal and jamming

    信号类型 参数 赋值
    LFM 时宽 10 μs
    带宽 50 MHz
    采样频率 125 MHz
    $ {J_0} $ NCJ 干扰带宽 60 MHz
    高斯白噪声 N(0, 1)
    $ {J_1} $ SNJ 干扰个数 4~7个
    干扰时宽 1.5~2.5 μs
    干扰占空比 0.8
    高斯白噪声 N(0, 1)
    $ {J_2} $ CSJ 转发次数 2~4次
    切片时宽 2.5~5.0 μs
    干扰占空比 0.9
    干扰时延 0.5 μs
    $ {J_3} $ COMBJ 干扰个数 9~17个
    干扰带宽 10~40 MHz
    $ {J_4} $ SMSPJ 干扰个数 4~7个
    干扰时延 1~3 μs
    $ {J_5} $ DFTJ 假目标个数 5~10个
    假目标时延 1~3 μs
    $ {J_6} $ ISCJ 转发次数 2~4次
    干扰个数 1~4个
    切片时宽 1~3 μs
    干扰时延 0.5~1.0 μs
    $ {J_7} $ MISRJ 转发次数 3~5次
    切片时宽 6~8 μs
    干扰占空比 0.95
    干扰时延 0.5~1.0 μs
    $ {J_8} $ NCJ+DFTJ 由单一干扰参数决定
    $ {J_9} $ SNJ+DFTJ
    $ {J_{10}} $ CSJ+SMSPJ
    $ {J_{11}} $ COMBJ+ISCJ
    $ {J_{12}} $ MISRJ+SMSPJ
    $ {J_{13}} $ MISRJ+ISCJ
    注:“N(0, 1)”表示0为平均数、以1为标准差的正态分布。
    下载: 导出CSV

    表  4  不同超参数${\boldsymbol{\lambda}} $对本文提出方法的识别准确率的影响(%)

    Table  4.   The impact of different hyperparameters ${\boldsymbol{\lambda}} $ on the recognition accuracy of the proposed method (%)

    超参数$\lambda $取值 准确率
    0.2 85.17
    0.4 88.79
    0.6 91.80
    0.8 93.55
    1.0 93.09
    注:表4中使用粗体标出了当前实验中最优的结果,下划线标出了当前实验中次优的结果。
    下载: 导出CSV

    表  5  不同超参数${\boldsymbol{\tau}} $对本文提出方法的识别准确率的影响(%)

    Table  5.   The impact of different hyperparameters ${\boldsymbol{\tau}} $ on the recognition accuracy of the proposed method (%)

    超参数$\tau $取值 准确率
    0.25 60.26
    0.50 85.17
    0.75 93.55
    1.00 88.83
    1.25 88.13
    1.50 73.11
    1.75 70.44
    2.00 65.60
    注:表5中使用粗体标出了当前实验中最优的结果,下划线标出了当前实验中次优的结果。
    下载: 导出CSV

    表  6  有效性验证实验结果

    Table  6.   Validity verification experiment results

    多域浅层特征 时频图像深层特征 特征联合方式 准确率(%) 单次训练时间(s)
    × 80.11 0.203
    特征拼接 85.63 0.470
    特征对比(本文方法) 93.55 0.317
    注:“×”表示未使用相应的特征,“√”表示使用了相应的特征,并使用粗体标出了最优的结果,下划线标出了次优的结果。
    下载: 导出CSV

    表  7  不同方法的识别性能对比结果(均值±标准差)

    Table  7.   Experimental results of jamming recognition performance evaluation between the proposed method and the comparative methods (mean ± standard deviation)

    干扰类别和性能评价指标 干扰识别方法(%)
    SVM 2D-CNN DFM MAML JR-TFSAD 本文方法
    干扰类别 NCJ 61.57±8.97 97.74±0.34 100.00±0.00 98.33±0.15 98.97±0.00 96.92±0.37
    SNJ 94.55±6.74 82.97±1.50 71.23±0.15 73.67±0.36 89.23±0.15 86.15±1.45
    CSJ 96.16±6.02 100.00±0.00 100.00±0.00 100.00±0.00 100.00±0.00 100.00±0.00
    COMBJ 89.44±4.67 37.95±0.00 48.21±0.27 68.99±0.56 31.79±0.73 100.00±0.00
    SMSPJ 91.67±9.64 90.15±4.86 100.00±0.00 85.67±0.39 73.33±0.00 100.00±0.00
    DFTJ 48.23±7.81 45.13±0.00 65.85±0.25 94.98±0.19 100.00±0.00 92.30±0.80
    ISCJ 77.22±7.11 37.44±0.00 60.00±0.13 92.00±0.20 92.31±0.24 99.49±0.09
    MISRJ 39.29±4.13 99.03±0.15 78.97±0.11 80.67±0.50 92.31±0.00 93.85±0.57
    NCJ+DFTJ 62.12±9.82 100.00±0.00 95.90±0.05 100.00±0.00 80.00±0.00 100.00±0.00
    SNJ+DFTJ 91.97±8.29 100.00±0.00 84.77±0.83 99.67±0.23 97.95±0.00 95.90±0.55
    CSJ+SMSPJ 64.39±8.36 68.21±0.00 41.54±0.98 75.33±0.20 88.21±0.25 98.97±0.15
    COMBJ+ISCJ 56.21±9.95 100.00±0.00 58.15±0.25 54.02±0.47 77.95±0.00 67.70±2.08
    MISRJ+SMSPJ 40.96±8.05 57.03±1.60 36.56±0.33 49.00±0.48 77.44±0.25 92.82±0.81
    MISRJ+ISCJ 63.38±8.84 23.59±6.62 97.95±0.13 93.33±0.17 71.28±0.26 85.64±1.65
    识别结果 准确率(%) 69.80±1.42 73.41±0.42 74.23±0.26 83.26±0.45 83.55±0.22 93.55±0.29
    召回率(%) 69.97±1.57 73.74±0.34 74.64±0.22 83.35±0.39 83.64±0.23 93.61±0.27
    F1(%) 68.23±1.74 72.75±0.39 73.15±0.15 83.59±0.56 82.98±0.22 93.14±0.28
    精准率(%) 70.36±1.43 71.14±0.29 76.01±0.25 86.79±0.36 84.05±0.21 94.79±0.30
    Kappa(×100) 67.47±1.45 72.25±0.45 72.24±0.37 82.07±0.39 82.29±0.22 93.05±0.31
    模型
    复杂度
    浮点运算次(109) 1.456 2.816 0.273 11.978 3.819
    模型参数量(106) 15.471 18.862 1.076 18.370 21.254
    单次训练平均时间(s) 0.197 0.270 0.127 51.200 0.317
    单次测试平均时间(s) 13.738 18.798 2.100 12.029 9.933
    注:每类干扰训练样本量为2;表中使用粗体标出了最优的结果,下划线标出了次优的结果。
    下载: 导出CSV

    表  8  不同方法在不同训练样本下的平均识别准确率±标准差比较(%)

    Table  8.   Comparison of mean recognition accuracy ± standard deviation of different methods under different numbers of training samples (%)

    干扰识别方法 每类干扰训练样本量
    2个(占1%) 3个 4个 5个
    SVM 69.80±1.42 75.63±0.96 80.95±0.36 82.78±0.46
    2D-CNN 73.41±0.42 83.33±0.31 89.13±0.03 93.32±0.12
    DFM 74.23±0.06 81.12±0.47 90.03±0.56 95.02±0.28
    MAML 83.26±0.42 90.67±0.35 95.43±0.21 97.85±0.26
    JR-TFSAD 83.55±0.12 88.70±0.30 93.40±0.29 98.91±0.41
    本文方法 93.55±0.29 97.68±0.16 99.62±0.03 100.00±0.00
    注:表8中使用粗体标出了最优的结果,下划线标出了次优的结果。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-06-21
  • 修回日期:  2024-08-12
  • 网络出版日期:  2024-08-28
  • 刊出日期:  2024-09-28

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