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基于时频重排算法的LPI雷达信号识别

姬丽彬 朱岩 崔天舒 王栋 黄永辉

李娜, 赵慧洁. 基于形态学与正交子空间投影的端元提取方法[J]. 北京航空航天大学学报, 2010, 36(12): 1457-1460.
引用本文: 姬丽彬,朱岩,崔天舒,等. 基于时频重排算法的LPI雷达信号识别[J]. 北京航空航天大学学报,2025,51(4):1324-1331 doi: 10.13700/j.bh.1001-5965.2023.0218
Li Na, Zhao Huijie. Endmember extraction method based on morphology and orthogonal subspace projection[J]. Journal of Beijing University of Aeronautics and Astronautics, 2010, 36(12): 1457-1460. (in Chinese)
Citation: JI L B,ZHU Y,CUI T S,et al. LPI radar signal recognition based on time-frequency reassignment algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics,2025,51(4):1324-1331 (in Chinese) doi: 10.13700/j.bh.1001-5965.2023.0218

基于时频重排算法的LPI雷达信号识别

doi: 10.13700/j.bh.1001-5965.2023.0218
基金项目: 中国科学院复杂航天系统电子信息技术重点实验室自主部署基金(Y42613A32S)
详细信息
    通讯作者:

    E-mail:zhuyan@nssc.ac.cn

  • 中图分类号: TN974

LPI radar signal recognition based on time-frequency reassignment algorithm

Funds: Independent Deployment Foundation of Key Laboratory of Electronic Information Technology for Complex Space Systems, Chinese Academy of Sciences (Y42613A32S)
More Information
  • 摘要:

    针对低截获概率(LPI)雷达信号在低信噪比(SNR)情况下识别效果不理想、网络模型复杂的问题,提出一种基于时频重排和多尺度残差网络的LPI雷达信号识别方法。该方法以魏格纳-维尔分布(WVD)为基础,通过时频重排算法提高信号的聚集性,得到信号的时频分布图像,输入到多尺度残差网络中完成信号的分类。通过构建多径莱斯衰落信道完成复杂电磁环境仿真,实验结果表明:所提方法在SNR为−8 dB时,对Costas、Frank、P1~P4等13类LPI雷达典型调制样式能达到94%的识别准确率,相比其他方法在低信噪比下具有更好的识别性能。

     

  • 图 1  SNR为10 dB时13种雷达信号的RSPWVD时频图像

    Figure 1.  RSPWVD time-frequency images of 13 kinds of radar signals with SNR of 10 dB

    图 2  图像预处理流程

    Figure 2.  Flow chart of image preprocessing

    图 3  多尺度残差网络模型

    Figure 3.  Multi-scale residual network model

    图 4  LPI雷达信号调制识别流程

    Figure 4.  LPI radar signal modulation recognition process

    图 5  13种雷达调制方式的识别准确率

    Figure 5.  Recognition accuracy of 13 radar modulation modes

    图 6  信号的混淆矩阵(−8 dB)

    Figure 6.  Signal confusion matrix (−8 dB)

    图 7  6种方法识别准确率对比

    Figure 7.  Comparison of recognition accuracy of six methods

    表  1  波形参数设置

    Table  1.   Waveform parameters setting

    LPI信号调制样式 调制参数 取值范围
    All 载频fc/Hz U(fs /6, fs/5)
    线性调频 带宽B/Hz
    采样个数N
    U(fs/20,fs/15)
    [512,1024]
    Rect 采样个数N [512,1024]
    Costas 跳频序列长度
    基础频率fmin/Hz
    采样个数N
    {3,4,5,6}
    U(fs/32, fs/25)
    [512,1024]
    Barker 码长L
    相位子波数cpp
    {7,11,13}
    [25]
    Frank 相位子波数cpp
    频率步长M
    [35]
    {6,7,8}
    P1,P2 相位子波数cpp
    频率步长M
    [35]
    {6,8}
    P3,P4 相位子波数cpp
    子码数ns
    [35]
    {36,64}
    T1,T2 相位状态数Np
    相位波形段数Ng
    采样个数N
    2
    {4,5,6}
    [512,1024]
    T3,T4 相位状态数Np
    相位波形段数Ng
    采样个数N
    调制带宽B/Hz
    2
    {4,5,6}
    [512,1024]
    U(fs/20,fs/15)
    下载: 导出CSV

    表  2  信道参数设置

    Table  2.   Channel parameters setting

    信道参数 取值范围
    采样频率/Hz fs
    多径衰减/ns U(1,1000)
    平均路径增益/dB U(−20,0)
    K-factor 4
    最大多普勒频移/Hz U(10, 1000)
    下载: 导出CSV

    表  3  运行时间对比

    Table  3.   Comparison of running time

    时频方法时间/s
    本文方法0.269 3
    CWD0.307 2
    WVD0.039 2
    STFT0.064 6
    SPWVD0.264 1
    MSST0.199 3
    下载: 导出CSV
  • [1] PACE P E. Detecting and classifying low probability of intercept radar[M]. 2nd ed. Boston: Artech House, 2009.
    [2] LIU Y J, XIAO P, WU H C, et al. LPI radar signal detection based on radial integration of Choi-Williams time-frequency image[J]. Journal of Systems Engineering and Electronics, 2015, 26(5): 973-981. doi: 10.1109/JSEE.2015.00106
    [3] XIAO Z L, YAN Z Y. Radar emitter identification based on novel time-frequency spectrum and convolutional neural network[J]. IEEE Communications Letters, 2021, 25(8): 2634-2638. doi: 10.1109/LCOMM.2021.3084043
    [4] QUAN D Y, TANG Z Y, WANG X F, et al. LPI radar signal recognition based on dual-channel CNN and feature fusion[J]. Symmetry, 2022, 14(3): 570. doi: 10.3390/sym14030570
    [5] WANG X Z. Electronic radar signal recognition based on wavelet transform and convolution neural network[J]. Alexandria Engineering Journal, 2022, 61(5): 3559-3569. doi: 10.1016/j.aej.2021.09.002
    [6] CHEN K Y, ZHANG J Y, CHEN S, et al. Automatic modulation classification of radar signals utilizing X-net[J]. Digital Signal Processing, 2022, 123: 103396. doi: 10.1016/j.dsp.2022.103396
    [7] YUAN S B, LI P, WU B, et al. Semi-supervised classification for intra-pulse modulation of radar emitter signals using convolutional neural network[J]. Remote Sensing, 2022, 14(9): 2059. doi: 10.3390/rs14092059
    [8] ZHANG M, DIAO M, GAO L P, et al. Neural networks for radar waveform recognition[J]. Symmetry, 2017, 9(5): 75. doi: 10.3390/sym9050075
    [9] WANG G, CHEN S, HUANG J, et al. Radar signal sorting and recognition based on transferred deep learning[J]. Computer Science and Application, 2019, 9(9): 1761-1778. doi: 10.12677/CSA.2019.99198
    [10] BOASHASH B. Time frequency signal analysis and processing: a comprehensive reference[M]. Amsterdam: Elsevier, 2003.
    [11] 杨瑾, 郝新红, 陈齐乐. 基于多域特征的多雷达信号自动识别方法[J]. 北京航空航天大学学报, 2024, 50(3): 931-939.

    YANG J, HAO X H, CHEN Q L. Automatic recognition method of multi-radar signals based on multi-domain features[J]. Journal of Beijing University of Aeronautics and Astronautics, 2024, 50(3): 931-939(in Chinese).
    [12] AUGER F, FLANDRIN P. Improving the readability of time-frequency and time-scale representations by the reassignment method[J]. IEEE Transactions on Signal Processing, 1995, 43(5): 1068-1089. doi: 10.1109/78.382394
    [13] WANG Y, WU X, LI W Z, et al. Analysis of micro-Doppler signatures of vibration targets using EMD and SPWVD[J]. Neurocomputing, 2016, 171: 48-56. doi: 10.1016/j.neucom.2015.06.005
    [14] ATAIE R, EMRANI ZARANDI A A, SAFAEI MEHRABANI Y. An efficient inexact full adder cell design in CNFET technology with high-PSNR for image processing[J]. International Journal of Electronics, 2019, 106(6): 928-944. doi: 10.1080/00207217.2019.1576232
    [15] 崔天舒, 崔凯, 黄永辉, 等. 卷积神经网络卫星信号自动调制识别算法[J]. 北京航空航天大学学报, 2022, 48(6): 986-994.

    CUI T S, CUI K, HUANG Y H, et al. Convolutional neural network based algorithm for automatic modulation recognition of satellite signals[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(6): 986-994(in Chinese).
    [16] 陈永, 陈锦, 陶美风. 多尺度特征和注意力融合的生成对抗壁画修复[J]. 北京航空航天大学学报, 2023, 49(2): 254-264.

    CHEN Y, CHEN J, TAO M F. Mural inpainting with generative adversarial networks based on multi-scale feature and attention fusion[J]. Journal of Beijing University of Aeronautics and Astronautics, 2023, 49(2): 254-264(in Chinese).
    [17] GAO J P, LU Y, QI J W, et al. A radar signal recognition system based on non-negative matrix factorization network and improved artificial bee colony algorithm[J]. IEEE Access, 2019, 7: 117612-117626. doi: 10.1109/ACCESS.2019.2936669
    [18] KONG S H, KIM M, HOANG L M, et al. Automatic LPI radar waveform recognition using CNN[J]. IEEE Access, 2018, 6: 4207-4219. doi: 10.1109/ACCESS.2017.2788942
    [19] HOANG L M, KIM M, KONG S H. Automatic recognition of general LPI radar waveform using SSD and supplementary classifier[J]. IEEE Transactions on Signal Processing, 2019, 67(13): 3516-3530. doi: 10.1109/TSP.2019.2918983
    [20] 王功明, 陈世文, 黄洁, 等. 基于多重同步压缩变换的雷达辐射源分选识别[J]. 现代雷达, 2020, 42(3): 49-56.

    WANG G M, CHEN S W, HUANG J, et al. Radar emitter sorting and recognition based on multi-synchrosqueezing transform[J]. Modern Radar, 2020, 42(3): 49-56(in Chinese).
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出版历程
  • 收稿日期:  2023-04-27
  • 录用日期:  2023-07-21
  • 网络出版日期:  2023-08-01
  • 整期出版日期:  2025-04-30

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