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基于MSST及HOG特征提取的雷达辐射源信号识别

全大英 唐泽雨 陈赟 楼维中 汪晓锋 章东平

全大英,唐泽雨,陈赟,等. 基于MSST及HOG特征提取的雷达辐射源信号识别[J]. 北京航空航天大学学报,2023,49(3):538-547 doi: 10.13700/j.bh.1001-5965.2022.0338
引用本文: 全大英,唐泽雨,陈赟,等. 基于MSST及HOG特征提取的雷达辐射源信号识别[J]. 北京航空航天大学学报,2023,49(3):538-547 doi: 10.13700/j.bh.1001-5965.2022.0338
QUAN D Y,TANG Z Y,CHEN Y,et al. Radar emitter signal recognition based on MSST and HOG feature extraction[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(3):538-547 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0338
Citation: QUAN D Y,TANG Z Y,CHEN Y,et al. Radar emitter signal recognition based on MSST and HOG feature extraction[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(3):538-547 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0338

基于MSST及HOG特征提取的雷达辐射源信号识别

doi: 10.13700/j.bh.1001-5965.2022.0338
基金项目: 浙江省自然科学基金(LQ20F020021);浙江省电磁波信息技术与计量检测重点实验室开放式项目(2019KF0003)
详细信息
    通讯作者:

    E-mail:qdy@cjlu.edu.cn

  • 中图分类号: TN974

Radar emitter signal recognition based on MSST and HOG feature extraction

Funds: Zhejiang Provincial Natural Science Foundation of China (LQ20F020021); Open Project Funding of the Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province (2019KF0003)
More Information
  • 摘要:

    针对传统雷达信号识别算法在低信噪比下识别准确率低的问题,提出了基于多重同步压缩(MSST)时频变换及方向梯度直方图(HOG)特征提取的雷达辐射源信号识别算法。所提算法在雷达时域信号短时傅里叶变换(STFT)基础上进行多重同步压缩处理获得信号时频分布图,通过HOG算子对信号时频分布图进行HOG特征提取,将提取的HOG特征通过主成分分析法(PCA)进行降维,将降维后的特征参数送入支持向量机(SVM)对雷达信号进行分类与识别。实验结果表明:所提算法具有较低的复杂度,当信噪比为−8 dB时,仿真实验与半实物仿真实验针对9种典型雷达信号的识别准确率达到90%以上。

     

  • 图 1  九种雷达信号的MSST时频图

    Figure 1.  MSST time-frequency images of 9 kinds of radar signals

    图 2  图像预处理流程

    Figure 2.  Flow of image preprocessing

    图 3  梯度方向分块

    Figure 3.  Gradient direction block

    图 4  方向梯度直方图

    Figure 4.  Histogram of oriented gradient

    图 5  算法整体流程

    Figure 5.  Overall flow chart of the proposed algorithm

    图 6  STFT、SST和MSST时频图

    Figure 6.  Time-frequency images of STFT, SST and MSST

    图 7  识别准确率

    Figure 7.  Recognition accuracy

    图 8  九种雷达信号识别准确率混淆矩阵

    Figure 8.  Confusion matrix of recognition rate of 9 kinds of radar signals

    图 9  四种算法平均识别准确率对比

    Figure 9.  Comparison of average recognition rates of four algorithms

    图 10  雷达辐射源信号识别半实物仿真实验平台

    Figure 10.  Hardware-in-the-loop simulation experiment platform for radar radiation source signal recognition

    图 11  基于半实物仿真实测数据的识别准确率

    Figure 11.  Recognition accuracy based on hardware-in-the-loop simulation data

    表  1  梯度方向和梯度值

    Table  1.   Gradient direction and gradient values

    梯度方向/(°)9015080701010514030
    梯度值30684383612178
    下载: 导出CSV

    表  2  算法训练时间

    Table  2.   Algorithm training time

    算法训练
    样本
    训练时间/s
    信噪比
    −8 dB
    信噪比
    −6 dB
    信噪比
    −4 dB
    信噪比
    −2 dB
    信噪比
    0 dB
    AlexNet3600867865862861853
    SVM36006766615754
    下载: 导出CSV

    表  3  算法识别时间

    Table  3.   Algorithm recognition time

    算法HOG+SVM LBPV+SVMHolder+SVM
    识别时间/s394820
    下载: 导出CSV

    表  4  训练集和测试集在不同信噪比下的整体识别准确率

    Table  4.   Overall recognition rate of training set and test set under different SNR

    训练集信噪比/dB测试集信噪比/dB总体识别准确率/%
    −14−693.06
    210100
    −2−1090.22
    10297.61
    −14~10−14~1092.02
    下载: 导出CSV
  • [1] KISHORE T R, RAO K D. Automatic intrapulse modulation classification of advanced LPI radar waveforms[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(2): 901-914. doi: 10.1109/TAES.2017.2667142
    [2] IGLESIAS V, GRAJAL J, ROYER P, et al. Real-time low complexity automatic modulation classifier for pulsed radar signals[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(1): 108-126. doi: 10.1109/TAES.2014.130183
    [3] SCHLEHER D C. LPI radar: Fact or fiction[J]. IEEE Aerospace and Electronic Systems Magazine, 2006, 21(5): 3-6. doi: 10.1109/MAES.2006.1635166
    [4] 黄颖坤, 金炜东, 葛鹏, 等. 基于多尺度信息熵的雷达辐射源信号识别[J]. 电子与信息学报, 2019, 41(5): 1084-1091. doi: 10.11999/JEIT180535

    HUANG Y K, JIN W D, GE P, et al. Radar emitter signal identification based on multi-scale information entropy[J]. Journal of Electronics & Information Technology, 2019, 41(5): 1084-1091(in Chinese). doi: 10.11999/JEIT180535
    [5] 徐卓君, 杨雯婷, 杨承志, 等. 雷达脉内调制识别的改进残差神经网络算法[J]. 吉林大学学报(工学版), 2021, 51(4): 1454-1460.

    XU Z J, YANG W T, YANG C Z, et al. Improved residual neural network algorithm for radar intrapulse modulation recognition[J]. Journal of Jilin University (Engineering Edition), 2021, 51(4): 1454-1460(in Chinese).
    [6] 刘鲁涛, 戴亮军, 陈涛. 基于频谱复杂度的雷达信号调制方式识别[J]. 哈尔滨工程大学学报, 2018, 39(6): 1081-1086. doi: 10.11990/jheu.201708034

    LIU L T, DAI L J, CHEN T. Radar signal modulation recognition based on spectrum complexity[J]. Journal of Harbin Engineering University, 2018, 39(6): 1081-1086(in Chinese). doi: 10.11990/jheu.201708034
    [7] 谢存祥, 张立民, 钟兆根. 基于时频特征提取和残差神经网络的雷达信号识别[J]. 系统工程与电子技术, 2021, 43(4): 917-926.

    XIE C X, ZHANG L M, ZHONG Z G. Radar signal recognition based on time-frequency feature extraction and residual neural network[J]. Systems Engineering and Electronics, 2021, 43(4): 917-926(in Chinese).
    [8] 白航, 赵拥军, 胡德秀, 等. 基于Choi-Williams时频图像特征的雷达辐射源识别[J]. 数据采集与处理, 2012, 27(4): 480-485. doi: 10.3969/j.issn.1004-9037.2012.04.014

    BAI H, ZHAO Y J, HU D X, et al. Radar emitter recognition based on image feature of Choi-Williams time-frequency distribution[J]. Data Acquisition and Processing, 2012, 27(4): 480-485(in Chinese). doi: 10.3969/j.issn.1004-9037.2012.04.014
    [9] OBERLIN T, MEIGNEN S, PERRIER V. The Fourier-based synchrosqueezing transform[C]//2014 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE Press, 2014: 315-319.
    [10] 倪雪, 王华力, 徐志军, 等. 基于STFT-SST和深度卷积网络的多相码雷达信号识别[J]. 数据采集与处理, 2020, 35(6): 1090-1096.

    NI X, WANG H L, XU Z J, et al. Polyphase codes radar signal recognition based on STFT-SST and deep convolutional network[J]. Data Acquisition and Processing, 2020, 35(6): 1090-1096(in Chinese).
    [11] YU G, WANG Z, ZHAO P. Multisynchrosqueezing transform[J]. IEEE Transactions on Industrial Electronics, 2019, 66(7): 5441-5455. doi: 10.1109/TIE.2018.2868296
    [12] LI D X, JIA H Y, YE Y C, et al. High power microwave signal detection based on second order multisynchrosqueezing transform[J]. Journal of Physics:Conference Series, 2020, 1617: 012049. doi: 10.1088/1742-6596/1617/1/012049
    [13] 王功明. 雷达信号脉内特征分析与识别关键技术研究[D]. 郑州: 战略支援部队信息工程大学, 2019.

    WANG G M. Research on key technology of radar signal intra-pulse features analysis and recognition[D]. Zhengzhou: Strategic Support Force Information Engineering University, 2019(in Chinese).
    [14] 钱红艳. 多重同步压缩变换的提升算法及其在地震信号处理中的应用[D]. 成都: 成都理工大学, 2020.

    QIAN H Y. Lifting algorithm of multiple synchronous compression transform and its application in seismic signal processing[D]. Chengdu: Chengdu University of Technology, 2020(in Chinese).
    [15] ATAIE R, ZARANDI A A E, MEHRABANI Y S. 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
    [16] 申彤, 庄建军, 黎文斯, 等. 基于HOG特征提取和支持向量机的东巴文识别[J]. 南京大学学报(自然科学), 2020, 56(6): 870-876.

    SHEN T, ZHUANG J J, LI W S, et al. Research on recognition of Dongba script by a combination of HOG feature extraction and support vector machine[J]. Journal of Nanjing University (Natural Sciences), 2020, 56(6): 870-876(in Chinese).
    [17] 孟凡杰, 唐宏, 王义哲, 等. 基于时频图像纹理特征的雷达辐射源信号识别[J]. 弹箭与制导学报, 2017, 37(3): 152-156. doi: 10.15892/j.cnki.djzdxb.2017.03.038

    MENG F J, TANG H, WANG Y Z, et al. Radar emitter signal recognition based on time-frequency image texture feature[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2017, 37(3): 152-156(in Chinese). doi: 10.15892/j.cnki.djzdxb.2017.03.038
    [18] 郭立民, 陈鑫, 陈涛. 基于AlexNet模型的雷达信号调制类型识别[J]. 吉林大学学报(工学版), 2019, 49(3): 1000-1008.

    GUO L M, CHEN X, CHEN T. Recognition of radar signal modulation type based on AlexNet model[J]. Journal of Jilin University (Engineering Edition), 2019, 49(3): 1000-1008(in Chinese).
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出版历程
  • 收稿日期:  2022-05-09
  • 录用日期:  2022-07-29
  • 网络出版日期:  2022-08-05
  • 整期出版日期:  2023-03-30

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