基于SVM的捷变频雷达密集转发干扰智能抑制方法

杜思予 刘智星 吴耀君 沙明辉 全英汇

杜思予, 刘智星, 吴耀君, 等. 基于SVM的捷变频雷达密集转发干扰智能抑制方法[J]. 雷达学报, 待出版. doi: 10.12000/JR22065
引用本文: 杜思予, 刘智星, 吴耀君, 等. 基于SVM的捷变频雷达密集转发干扰智能抑制方法[J]. 雷达学报, 待出版. doi: 10.12000/JR22065
DU Siyu, LIU Zhixing, WU Yaojun, et al. Dense-repeated jamming suppression algorithm based on the support vector machine for frequency agility radar[J]. Journal of Radars, in press. doi: 10.12000/JR22065
Citation: DU Siyu, LIU Zhixing, WU Yaojun, et al. Dense-repeated jamming suppression algorithm based on the support vector machine for frequency agility radar[J]. Journal of Radars, in press. doi: 10.12000/JR22065

基于SVM的捷变频雷达密集转发干扰智能抑制方法

doi: 10.12000/JR22065
基金项目: 国家自然科学基金(61772397),陕西省杰出青年科学基金(2021JC-23),陕西省科技创新团队(2019TD-002)
详细信息
    作者简介:

    杜思予,硕士研究生,主要研究方向为雷达波形优化及抗干扰

    刘智星,博士研究生,主要研究方向为捷变相参雷达信号处理及抗干扰

    吴耀君,副研究员,博士研究生,主要研究方向为捷变雷达抗干扰、雷达目标特性识别、新体制雷达

    沙明辉,研究员,硕士,主要研究方向为雷达系统设计和雷达电子对抗

    全英汇,教授,博士,主要研究方向为电磁博弈对抗、敏捷雷达、雷达遥感等

    通讯作者:

    全英汇 yhquan@mail.xidian.edu.cn

  • 责任主编:刘泉华 Corresponding Editor: LIU Quanhua
  • 中图分类号: TN972

Dense-repeated Jamming Suppression Algorithm Based on the Support Vector Machine for Frequency Agility Radar

Funds: The National Natural Science Foundation of China (61772397), The Shaanxi Provincial Science Fund for Distinguished Young Scholars (2021JC-23), The Science and Technology Innovation Team of Shaanxi Province (2019TD-002)
More Information
  • 摘要: 密集转发干扰与雷达发射信号高度相关,兼具压制式和欺骗式干扰效果,使雷达系统难以检测到真实目标,严重威胁雷达作战能力。针对这一问题,该文提出一种基于支持向量机(SVM)的捷变频雷达密集转发干扰智能抑制方法。通过对随机样本集进行离线训练获得最优SVM模型,智能化识别并分类目标和干扰;然后,采用平滑滤波进一步抑制目标所在距离单元内的干扰信号;最后,基于压缩感知(CS)理论进行二维高分辨重构,估计出目标参数信息。仿真实验与实测数据处理结果表明,所提算法在不同场景下均能够有效抑制密集转发干扰,准确检测出真实目标。

     

  • 图  1  捷变频雷达信号模型

    Figure  1.  Frequency agile radar signal model

    图  2  密集转发干扰原理图

    Figure  2.  Dense repeated jamming principle diagram

    图  3  干扰抑制算法流程图

    Figure  3.  Flow chart of interference suppression algorithm

    图  4  匹配滤波数据空间分布特征

    Figure  4.  Matching filtering data spatial distribution characteristics

    图  5  特征参数计算示意图

    Figure  5.  Schematic diagram of characteristic parameter calculation

    图  6  平滑滤波

    Figure  6.  Smoothing window filtering

    图  7  最优SVM分类模型

    Figure  7.  Optimal SVM classification model

    图  8  抗干扰仿真结果

    Figure  8.  Anti-jamming simulation results

    图  9  实测数据处理结果

    Figure  9.  Measured data processing results

    图  10  分类准确率随JSR变化曲线

    Figure  10.  The curve of classification accuracy changing with JSR

    图  11  分类准确率随训练样本比例变化曲线

    Figure  11.  The curve of classification accuracy changing with the proportion of training set

    图  12  目标信息保留度

    Figure  12.  The target information retention percentage

    图  13  不同算法在不同JSR下的检测概率

    Figure  13.  Detection probability of different algorithmsunder different JSR

    图  14  不同虚警率下检测概率随JSR变化曲线

    Figure  14.  The curve of detection probability changing with JSR under different false alarm rates

    表  1  雷达参数

    Table  1.   Radar parameters

    参数数值参数数值
    脉冲数Q64脉冲重复周期Tr40 μs
    信号脉宽Tp4 μs信号带宽B20 MHz
    初始载频fc14 GHz跳频间隔$ \Delta f$9 MHz
    采样率fs40 MHz
    下载: 导出CSV

    表  2  外场试验参数

    Table  2.   Outfield experiment parameters

    参数数值参数数值
    脉冲数Q128脉冲重复周期Tr250 μs
    信号脉宽Tp4 μs信号带宽B20 MHz
    跳频总数$Q' $256载频跳变范围33.2~34.2 GHz
    采样率fs60 MHz
    下载: 导出CSV
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  • 收稿日期:  2022-04-02
  • 修回日期:  2022-06-07
  • 网络出版日期:  2022-06-28

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