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一种小样本数据驱动的雷达复合干扰轻量化感知网络

郎彬 王欢 宫健

郎彬,王欢,宫健. 一种小样本数据驱动的雷达复合干扰轻量化感知网络[J]. 北京航空航天大学学报,2024,50(3):1005-1014 doi: 10.13700/j.bh.1001-5965.2022.0343
引用本文: 郎彬,王欢,宫健. 一种小样本数据驱动的雷达复合干扰轻量化感知网络[J]. 北京航空航天大学学报,2024,50(3):1005-1014 doi: 10.13700/j.bh.1001-5965.2022.0343
LANG B,WANG H,GONG J. A small sample data-driven radar compound jamming lightweight perception network[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(3):1005-1014 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0343
Citation: LANG B,WANG H,GONG J. A small sample data-driven radar compound jamming lightweight perception network[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(3):1005-1014 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0343

一种小样本数据驱动的雷达复合干扰轻量化感知网络

doi: 10.13700/j.bh.1001-5965.2022.0343
基金项目: 陕西省自然科学基金(2021JM-222)
详细信息
    通讯作者:

    E-mail:drgong@aliyun.com

  • 中图分类号: TN971.1

A small sample data-driven radar compound jamming lightweight perception network

Funds: Natural Science Foundation of Shaanxi Province (2021JM-222)
More Information
  • 摘要:

    基于深度学习的雷达干扰感知技术能精确感知各类雷达干扰类型,但需预先构建大规模且完备的训练样本,数据集构建工作量大、难度高,同时存在网络模型参数量较大、计算复杂度高的问题,导致在实际平台中难以应用。针对此问题,提出一种小样本数据驱动的雷达复合干扰轻量化感知网络,结合计算机视觉领域的“目标检测”思想建立干扰感知网络,利用雷达干扰时频分布数据提取多尺度特征图,预置锚框进行回归与分类,使用分组卷积与Ghost卷积对大参数量、高计算量的网络结构进行轻量化改进。实验结果表明:只需小规模的多种单一干扰模式样本,即可实现对单一干扰模式、两两复合模式及3类复合模式的灵活感知,在低干噪比条件下保持较高感知性能的同时大幅压缩模型的参数量与运算量。

     

  • 图 1  4类干扰信号的时频分布图像

    Figure 1.  Time frequency distribution image of four kinds of jamming signals

    图 2  YOLO系列网络工作流程

    Figure 2.  YOLO series network workflow

    图 3  S=4的特征图检测过程

    Figure 3.  Feature map detection process with S=4

    图 4  标准卷积与分组卷积

    Figure 4.  Standard convolution and group convolution

    图 5  Ghost卷积运算过程

    Figure 5.  Ghost convolution operation process

    图 6  C3Ghost模块结构

    Figure 6.  C3Ghost module structure

    图 7  雷达复合干扰灵活感知网络架构

    Figure 7.  Recognition network architecture of radar compound jamming

    图 8  4种网络在不同JNR下的mAP@0.5:0.95

    Figure 8.  MAP@0.5:0.95 of four networks under different JNR

    图 9  4种网络在不同JNR下的mAP@0.5

    Figure 9.  MAP@0.5 of four networks under different JNR

    图 10  JNR=0 dB时的混淆矩阵

    Figure 10.  Confusion matrix of JNR at 0 dB

    图 11  两两复合模式干扰感知效果

    Figure 11.  Jamming perception effect of two kinds of composite modes

    图 12  3类复合模式干扰感知效果

    Figure 12.  Jamming perception effect of three kinds of composite modes

    表  1  干扰信号主要仿真参数

    Table  1.   Jamming signal main simulation parameters

    JNR/dB 中心频率/
    MHz
    脉冲重复
    周期/μs
    带宽/MHz 采样频率/
    MHz
    0∶1∶16 10~40 100 40 200
    下载: 导出CSV

    表  2  YOLO v5s层级名称及参数量

    Table  2.   YOLO v5s level names and parameters quantity

    层级名称参数量
    1Focus3 520
    2Conv18 560
    3C318 816
    4Conv73 984
    5C3156 928
    6Conv295 424
    7C3625 152
    8Conv1 180 672
    9SPP656 896
    10C31 182 720
    11Conv131 584
    12Upsample0
    13Concat0
    14C3361 984
    15Conv33 024
    16Upsample0
    17Concat0
    18C390 880
    19Conv147 712
    20Concat0
    21C3296 448
    22Conv590 336
    23Concat0
    24C31 182 720
    25Detect24 273
    下载: 导出CSV

    表  3  本文网络各模块消融实验的mAP@0.5值

    Table  3.   MAP@0.5 value of ablation experiments for each module of the proposed network %

    Ghost G 参数量/106 浮点运算次数/109 JNR
    0 2 4 6 8 10 12 14 16
    7.06 16.4 95.5 98.6 98.7 97.8 96.5 98.5 97.6 97.9 99.0
    2 5.34 15.4 96.6 97.6 98.0 98.7 97.7 99.4 98.0 98.7 99.4
    5.93 15.9 98.3 99.1 99.1 98.9 98.1 99.1 98.3 98.4 99.4
    2 6.47 15.9 95.4 99.0 98.9 98.6 98.0 99.3 98.6 98.9 99.5
    4 5.05 15.2 97.0 97.8 97.8 97.9 97.4 98.0 97.2 98.6 99.3
    4 6.18 15.6 96.6 98.7 98.7 98.5 97.1 99.1 97.3 98.2 99.3
    下载: 导出CSV

    表  4  本文网络各模块消融实验的mAP@0.5:0.95值

    Table  4.   MAP@0.5:0.95 value of ablation experiments for each module of the proposed network %

    Ghost G 参数量/106 浮点运算次数/109 JNR
    0 2 4 6 8 10 12 14 16
    7.06 16.4 73.5 75.7 78.9 77.2 76.3 77.7 77.2 76.1 82.3
    2 5.34 15.4 74.1 76.6 79.2 78.5 78.1 80.5 77.7 80.6 86.4
    5.93 15.9 74.2 75.3 76.8 78.0 76.7 78.8 74.3 77.3 84.8
    2 6.47 15.9 73.6 76.1 80.1 79.4 78.7 79.3 78.5 78.8 84.8
    4 5.05 15.2 73.6 73.9 77.0 76.7 76.4 78.2 75.0 78.9 84.1
    4 6.18 15.6 72.9 76.6 79.0 77.9 76.1 79.1 76.6 77.0 82.6
    下载: 导出CSV

    表  5  10类复合干扰在不同JNR下的召回率感知结果

    Table  5.   Recall perceived results of 10 types of composite jamming under different JNR

    复合干扰类型 JNR
    0 2 4 6 8 10 12 14 16
    ISRJ+SMSP 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
    CSJ+NAJ 0.86 1.00 1.00 1.00 0.90 1.00 0.90 1.00 1.00
    NAJ+SMSP 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
    CSJ+ISRJ 0.90 1.00 1.00 1.00 1.00 0.96 1.00 1.00 1.00
    CSJ+SMSP 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
    ISRJ+NAJ 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
    CSJ+ISRJ+SMSP 0.87 0.93 1.00 0.93 1.00 1.00 0.96 1.00 1.00
    CSJ+ISRJ+NAJ 0.86 1.00 0.87 0.87 0.92 0.97 0.96 1.00 1.00
    ISRJ+NAJ+SMSP 0.96 0.98 1.00 0.95 1.00 1.00 1.00 1.00 1.00
    CSJ+SMSP+NAJ 0.97 1.00 0.93 1.00 0.93 1.00 0.98 1.00 1.00
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-05-10
  • 录用日期:  2022-06-20
  • 网络出版日期:  2022-06-24
  • 整期出版日期:  2024-03-27

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