面向小样本的多模态雷达有源欺骗干扰识别方法

张顺生 陈爽 陈晓莹 刘莹 王文钦

张顺生, 陈爽, 陈晓莹, 等. 面向小样本的多模态雷达有源欺骗干扰识别方法[J]. 雷达学报, 2023, 12(4): 882–891. doi: 10.12000/JR23104
引用本文: 张顺生, 陈爽, 陈晓莹, 等. 面向小样本的多模态雷达有源欺骗干扰识别方法[J]. 雷达学报, 2023, 12(4): 882–891. doi: 10.12000/JR23104
ZHANG Shunsheng, CHEN Shuang, CHEN Xiaoying, et al. Active deception jamming recognition method in multimodal radar based on small samples[J]. Journal of Radars, 2023, 12(4): 882–891. doi: 10.12000/JR23104
Citation: ZHANG Shunsheng, CHEN Shuang, CHEN Xiaoying, et al. Active deception jamming recognition method in multimodal radar based on small samples[J]. Journal of Radars, 2023, 12(4): 882–891. doi: 10.12000/JR23104

面向小样本的多模态雷达有源欺骗干扰识别方法

doi: 10.12000/JR23104
基金项目: 国家部委基金
详细信息
    作者简介:

    张顺生,博士,研究员,主要研究方向为新体制雷达探测与成像、人工智能技术在雷达、电子战中的应用等

    陈 爽,硕士生,主要研究方向为雷达有源欺骗干扰识别

    陈晓莹,博士生,主要研究方向为雷达信号处理、雷达抗干扰和认知雷达

    刘 莹,博士,教授,主要研究方向为数据挖掘、人工智能和高性能计算等

    王文钦,博士,教授,主要研究方向为新体制雷达、雷达信号处理和电子对抗技术

    通讯作者:

    张顺生 zhangss@uestc.edu.cn

  • 责任主编:毕大平 Corresponding Editor: BI Daping
  • 中图分类号: TN958

Active Deception Jamming Recognition Method in Multimodal Radar Based on Small Samples

Funds: The National Ministries Foundation
More Information
  • 摘要: 干扰识别是雷达抗干扰的前提,但对于实际的雷达欺骗干扰识别,存在着样本数量不足的问题。针对此问题,该文提出一种面向小样本的多模态雷达有源欺骗干扰识别方法。该方法基于雷达信号提取的特征参数及时频图像两种模态信息,利用原型网络训练多模态特征,并借助图像降噪处理和加权欧氏距离提高低信噪比下的识别性能,实现小样本条件下的雷达欺骗干扰识别。仿真结果表明,该文所提方法在干信比为3 dB时,10种雷达欺骗干扰的平均识别准确率达到了97%以上。模拟器数据的测试结果表明所提方法具备良好的泛化能力。

     

  • 图  1  预处理前的时频图像

    Figure  1.  Time-frequency images before preprocessing

    图  2  预处理后的时频图像

    Figure  2.  Time-frequency images after preprocessing

    图  3  原型网络基本架构图

    Figure  3.  Basic architecture of prototype network

    图  4  多模态特征融合网络

    Figure  4.  Multimodal feature fusion network

    图  5  本文方法的混淆矩阵(平均识别率97.65%)

    Figure  5.  Confusion matrix of the proposed method (average recognition rate 97.65%)

    图  6  不同网络在不同信噪比下识别性能比较

    Figure  6.  Comparison of recognition performance of different networks under different signal-to-noise ratios

    图  7  干扰信号数据获取平台

    Figure  7.  Interference signal data acquisition platform

    图  8  泛化能力测试结果

    Figure  8.  Generalization ability test results

    表  1  不同网络的复杂度参数表

    Table  1.   Table of complexity parameters for different networks

    网络FLOPs (G)Params (M)
    网络${\phi _{\rm{T}}}$0.0003399680.041760
    网络${\phi _{\rm{P}}}$0.1653754880.186048
    时频图像网络[23]0.2161582080.186048
    特征参数网络[24]0.0004085760.041760
    孪生网络[25]3.395630784138.357544
    下载: 导出CSV

    表  2  回波信号与欺骗干扰信号基本参数设置

    Table  2.   Basic parameter setting of echo signal and jamming signal

    信号类型参数参数值
    回波与干扰信号载波5.5 GHz
    带宽10 MHz
    采样率40 MHz
    脉冲宽度50 μs
    脉冲重复频率4000 Hz
    信噪比–10~10 dB
    干信比3 dB
    密集假目标干扰假目标数量3~7
    切片重构干扰矩形脉冲串的个数3~5
    每一段填充的时隙数3~5
    间歇采样转发干扰间歇采样脉冲宽度7~9 μs
    间歇采样周期12~15 μs
    频谱弥散干扰采样倍数3~5
    卷积灵巧噪声干扰噪声带宽倍数0.3~0.7
    距离拖引干扰拖引速度300~600 m/s
    速度拖引干扰拖引加速度50~200 m/s2
    距离速度联合拖引干扰拖引速度300~600 m/s
    拖引加速度50~200 m/s2
    下载: 导出CSV

    表  3  不同训练样本数下识别性能比较(%)

    Table  3.   Comparison of recognition performance under different numbers of training samples (%)

    训练信噪比2个训练样本5个训练样本10个训练样本15个训练样本20个训练样本
    –10 dB75.0292.1293.4692.2392.42
    –5 dB89.1491.1091.2993.2493.12
    0 dB86.1192.1892.2994.8993.27
    5 dB84.7492.8394.7989.0594.97
    10 dB83.1787.7387.8588.4190.67
    随机信噪比85.1893.9596.2597.5697.64
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-06-11
  • 修回日期:  2023-07-22
  • 网络出版日期:  2023-08-15
  • 刊出日期:  2023-08-28

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