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融合注意力机制的雷达欺骗干扰域适应识别方法

孙闽红 陈鑫伟 仇兆炀 滕旭阳

孙闽红, 陈鑫伟, 仇兆炀, 滕旭阳. 融合注意力机制的雷达欺骗干扰域适应识别方法[J]. 电子与信息学报, 2022, 44(11): 3891-3899. doi: 10.11999/JEIT210871
引用本文: 孙闽红, 陈鑫伟, 仇兆炀, 滕旭阳. 融合注意力机制的雷达欺骗干扰域适应识别方法[J]. 电子与信息学报, 2022, 44(11): 3891-3899. doi: 10.11999/JEIT210871
SUN Minhong, CHEN Xinwei, QIU Zhaoyang, TENG Xuyang. Radar Deception Jamming Recognition Method Based on Domain Adaptation and Attention Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3891-3899. doi: 10.11999/JEIT210871
Citation: SUN Minhong, CHEN Xinwei, QIU Zhaoyang, TENG Xuyang. Radar Deception Jamming Recognition Method Based on Domain Adaptation and Attention Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3891-3899. doi: 10.11999/JEIT210871

融合注意力机制的雷达欺骗干扰域适应识别方法

doi: 10.11999/JEIT210871
基金项目: 国防特色学科发展项目(JCKY2019415D002)
详细信息
    作者简介:

    孙闽红:男,博士,教授,研究方向为雷达通信信号处理及抗干扰

    陈鑫伟:男, 硕士生,研究方向为信号与信息处理

    仇兆炀:男, 博士,讲师,研究方向为信号与信息处理

    滕旭阳:男, 博士,讲师,研究方向为人工智能及机器学习

    通讯作者:

    陈鑫伟 779577409@qq.com

  • 中图分类号: TN974

Radar Deception Jamming Recognition Method Based on Domain Adaptation and Attention Mechanism

Funds: The Characteristic Discipline Program of National Defense (JCKY2019415D002)
  • 摘要: 针对目前雷达欺骗干扰识别中常规特征识别方法应用受限和训练高性能深度学习模型需要的大量标注样本难以高效获取的问题,该文提出一种基于对抗域适应网络的雷达欺骗干扰识别方法,以改善标签限制;并融合注意力机制残差模块进一步提升识别精度。首先,对雷达接收信号进行时频变换后,应用基于对抗网络思想的域适应技术实现从标注源域样本到未标注目标域样本的迁移识别。其次,通过所设计的空间通道注意力残差模块使网络训练聚焦于时频图全局空间特征和高响应通道,以忽略时频图像中可迁移性低的区域抑制负迁移的产生。在不同源域与目标域雷达欺骗干扰数据集上的实验结果表明了该方法的可行性和有效性。
  • 图  1  算法流程图

    图  2  DANN网络

    图  3  残差块结构

    图  4  注意力机制残差块结构

    图  5  Attention-RDANN网络结构

    图  6  Source_only的T-SNE降维可视化

    图  7  本文方法的T-SNE降维可视化

    图  8  不同条件下的欺骗干扰识别性能对比

    表  1  Hammerstein非线性模型参数表

    非线性系统参数r线性系统参数u
    r1r2r3r4u1u2u3
    真实发射机1–0.0695–0.0979–0.05530.98980.06240.0080
    欺骗干扰机0.9870–0.0690–0.0973–0.05490.98420.06190.0077
    下载: 导出CSV

    表  2  仿真参数设置

    信号参数参数值信号参数参数值
    调制类型LFM载频10 GHz
    脉宽20 μs信号带宽10 MHz
    采样频率40 MHz脉冲重复间隔100 μs
    距离欺骗时延2 μs速度欺骗多普勒频率偏2 kHz
    SMSP采样倍数4C&I子脉冲个数5
    C&I时隙数4
    下载: 导出CSV

    表  3  不同雷达欺骗干扰类型迁移效果对比

    源域→目标域本文方法仅源域训练源域→目标域本文方法仅源域训练
    Rd→Vd0.9600.502Vd→Rd0.9850.507
    Rd→R_Vd0.9950.991R_Vd→Rd0.9950.996
    Rd→SMSP0.9870.786SMSP→Rd0.9940.505
    Rd→C&I0.9860.759C&I→Rd0.9910.523
    Vd→R_Vd0.9810.506R_Vd→Vd0.9950.989
    Vd→SMSP0.9880.971SMSP→Vd0.9750.988
    Vd→C&I0.9870.969C&I→Vd0.9770.982
    R_Vd→SMSP0.9860.752SMSP→R_Vd0.9250.763
    R_Vd→C&I0.9850.785C&I→R_Vd0.9820.758
    C&I→SMSP0.9910.814SMSP→C&I0.9840.856
    下载: 导出CSV

    表  4  不同方法识别效果对比

    算法JNR(dB)单次平均识别时间(s)
    20→1010→20
    Zernike[20]0.7030.7013.20
    Zernike_TCA[21]0.6820.6843.51
    DAN[12]0.8010.7875.12
    JAN[13]0.9320.9254.98
    Source_only0.7970.7954.72
    DANN0.9490.9404.74
    Attention-RDANN0.9680.9624.89
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-24
  • 录用日期:  2022-03-03
  • 修回日期:  2022-02-21
  • 网络出版日期:  2022-03-09
  • 刊出日期:  2022-11-14

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