Radar Emitter Signal Recognition Based on Convolutional Neural Network and Coordinate Transformation of AmbiguityFunction Main Ridge

doi:10.3969/j.issn.1000-1093.2021.08.012

Abstract

Abstract: For the time-consuming and inconspicuous features of artificially extracting radar emitter signal features，a recognition method based on deep learning convolutional neural network and coordinate transformation of ambiguity function main ridge is proposed. The proposed method is used to extract the main ridge of ambiguity function of signal through fast discrete fractional Fourier transform，and then take the two-dimensional time-frequency image of polar coordinate domain of ambiguity function main ridge as the input of the convolutional neural network to realize the sorting and recognition of different radar signals. Simulation experimental results show that the proposed method not only maintains 100% recognition rate above 0 dB，but also stabilizes the recognition accuracy rate above 90% at -6 dB. Compared with traditional radar signal recognition methods and other deep learning-based recognition methods，the proposed method has greatly improved recognition rate and robustness，and has certain engineering application value.

Key words: radaremittersignalrecognition, deeplearning, convolutionalneuralnetwork, mainridgeofambiguityfunction

CLC Number:

TN974

PU Yunwei， LIU Taotao， GUO Jiang， WU Haixiao. Radar Emitter Signal Recognition Based on Convolutional Neural Network and Coordinate Transformation of AmbiguityFunction Main Ridge[J]. Acta Armamentarii, 2021, 42(8): 1680-1689.

References

［1］田春瑾，普运伟，郭媛蒲，等.基于图形轮廓的雷达信号模糊函数主脊切面特征提取方法［J］.兵工学报，2018，39(11):2280-2288.
TIAN C J，PU Y W，GUO Y P，et al.A novel feature extraction method for the slice of ambiguity function main ridge of radar signals based on graphic contour［J］.Acta Armamentarii，2018，39(11): 2280-2288.(in Chinese)
［2］王建华，杨延西，马晨. 基于价值函数的二维小波变换小波脊提取算法［J］. 仪器仪表学报，2017，38(12):2915-2923.
WANG J H，YANG Y X，MA C.Wavelet ridge extraction algorithm using a cost function in two-dimensional wavelet transform［J］.Chinese Journal of Scientific Instrument，2017，38(12):2915-2923.(in Chinese)
［3］王宗炼，任会兰，宁建国.基于小波变换的混凝土压缩损伤模式识别［J］.兵工学报，2017，38(9):1745-1753.
WANG Z L，REN H L，NING J G.Identification of damage modes of concrete under compressive loading based on wavelet transform［J］.Acta Armamentarii，2017，38(9):1745-1753.(in Chinese)
［4］王健，秦春霞，杨珂，等.基于导向重构与降噪稀疏自编码器的合成孔径雷达目标识别［J］.兵工学报，2020，41(9):1861-1870.
WANG J，QIN C X，YANG K，et al.A SAR target recognition algorithm based on guided fielter reconstruction and denoising sparse autoencoder［J］.Acta Armamentarii，2020，41(9):1861-1870.(in Chinese)
［5］胡越，罗东阳，花奎，等.关于深度学习的综述与讨论［J］.智能系统学报，2019，14(1):1-19.
HU Y，LUO D Y，HUA K，et al.Overview on deep learning［J］.CAAI Transactions on Intelligent Systems，2019，14(1):1-19.(in Chinese)
［6］黄智，王俊杰，石国良.基于时频分析和CNN的雷达辐射源识别算法［J］.舰船电子工程，2020，40(1):24-28.
HUANG Z，WANG J J，SHI G L.Radar emitter identification algorithm based on time frequency analysis and CNN［J］.Ship Electronic Engineering，2020，40(1):24-28.(in Chinese)
［7］ GAO J P，SHEN L X，GAO L P.Modulation recognition for radar emitter signals based on convolutional neural network and fusion features［J］.Transactions on Emerging Teleco-mmunications Technologies，2019，30(12):e3612.
［8］刘赢，田润澜，王晓峰.基于深层卷积神经网络和双谱特征的雷达信号识别方法［J］.系统工程与电子技术，2019，41(9):1998-2005.
LIU Y，TIAN R L，WANG X F.Radar signal recognition method based on deep convolutional neural network and bispectral features［J］.Systems Engineering and Electronics，2019，41(9):1998-2005.(in Chinese)
［9］侯文太，普运伟，郭媛蒲，等.基于高斯平滑与模糊函数等高线的雷达辐射源信号分选［J/OL］.自动化学报，2020［2020-09-01］.https:∥doi.org/10.16383/j.aas.c180739.
HOU W T，PU Y W，GUO Y P，et al.Radar emitter signal sorting based on Gaussian smoothing and ambiguity function contours［J/OL］. Acta Automatica Sinica, 2020［2020-09-01］.https:∥doi.org/10.16383/j.aas.c180739.(in Chinese)
［10］普运伟，金炜东，朱明，等.雷达辐射源信号模糊函数主脊切面特征提取方法［J］. 红外与毫米波学报，2008，27(2): 133-137.
PU Y W，JIN W D，ZHU M，et al.Feature extracting the main ridge slice characteristics of ambiguity function for radar emitter signals［J］.Journal of Infrared and Millimeter Waves，2008，27(2): 133-137.(in Chinese)
［11］ AKAY O，BOUDREAUX-BARTELS G F.Fractional convolution and correlation via operator methods and an application to detection of linear FM signals［J］.IEEE Transactions on Signal Processing，2001，49(5):979-993.
［12］ LE CUN Y，BOSER B，DENKER J S，et al.Handwritten digit recognition with a back propagation network［C］∥Proceedings of the 2nd International Conference on Neural Information Processing Systems.Cambridge，MA，US:MIT，1989:396-404.
［13］普运伟，侯文太，郭媛蒲，等. 基于模糊函数三维特征的雷达辐射源信号分选方法［J］. 控制与决策，2019，34(10):2178-2184.
PU Y W，HOU W T，GUO Y P，et al.A sorting method of radar emitter signal based on three dimensional feature of ambiguity function［J］.Control and Decision，2019，34(10):2178-2184.(in Chinese)
［14］ GUO Q，NAN P L，ZHANG X Y，et al.Recognition of radar emitter signals based on SVD and AF main ridge slice［J］.Journal of Communications and Networks，2015，17(5):491-498．
［15］曲志昱，毛校洁，侯长波.基于奇异值熵和分形维数的雷达信号识别［J］.系统工程与电子技术，2018，40(2):303-307.
QU Z Y，MAO X J，HOU C B.Radar signal recognition based on singular value entropy and fractal dimension［J］.Systems Engineering and Electronics，2018，40(2):303-307.(in Chinese)
［16］黄宇涛，普运伟，吴海潇，等.基于栈式自编码机和模糊函数主脊的雷达辐射源信号识别［J］.电波科学学报，2020，35(5): 689-698.
HUANG Y T，PU Y W，WU H X，et al. Radar emitter signal recognition based on stacked autoencoder and ambiguity function main ridge［J/OL］.Chinese Journal of Radio Science，2020，35(5): 689-698.（in Chinese）
［17］周志文，黄高明，高俊，等.一种深度学习的雷达辐射源识别算法［J］.西安电子科技大学学报，2017，44(3):77-82.
ZHOU Z W，HUANG G M，GAO J，et al. Radar emitter identification algorithm based on deep learning［J］.Journal of Xidian University，2017，44(3):77-82.(in Chinese)
［18］黄颖坤，金炜东，余志斌，等.基于深度学习和集成学习的辐射源信号识别［J］.系统工程与电子技术，2018，40(11):2420-2425.
HUANG Y K，JIN W D，YU Z B，et al. Radar emitter signal recognition based on deep learning and ensemble learning［J］.Systems Engineering and Electronics，2018，40(11):2420-2425.(in Chinese)

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