KISHORE T R , RAO K D . Automatic intrapulse modulation classification of advanced LPI radar waveforms [J ] . IEEE Transaction on Aerospace Electronic and Systems , 2017 , 53 ( 2 ): 901 - 904 . DOI: 10.1109/TAES.2017.2667142 http://doi.org/10.1109/TAES.2017.2667142 http://ieeexplore.ieee.org/document/7849221/ http://ieeexplore.ieee.org/document/7849221/

IGLESIAS V , GRAJAL J , ROYER P , et al . Real-time low-complexity automatic modulation classifier for pulsed radar signals [J ] . IEEE Transactions on Aerospace Electronic & Systems , 2015 , 51 ( 1 ): 108 - 126 .

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 http://doi.org/10.3390/sym14030570 https://www.mdpi.com/2073-8994/14/3/570 https://www.mdpi.com/2073-8994/14/3/570 The accuracy of low probability of intercept (LPI) radar waveform recognition is an important and challenging problem in electronic warfare. Aiming at the problem of the difficulty in feature extraction and the low recognition rates of the LPI radar signal under a low signal-to-noise ratio, and inspired by the symmetry theory, we propose a new approach for the LPI radar signal recognition method based on a dual-channel convolutional neural network (CNN) and feature fusion. Our new approach contains three main modules: the preprocessing module that converts the LPI radar waveforms into two-dimensional time-frequency images using the Choi–Williams distribution (CWD) transformation and performs image binarization, the feature extraction module that extracts different features obtained from the images, and the recognition module that utilizes a multi-layer perceptron (MLP) network to fuse these features and distinguish the type of LPI radar signals. In the feature extraction module, a two-channel CNN model is proposed that extracts Histogram of Oriented Gradients (HOG) features and deep features from time-frequency images, respectively. Finally, the recognition module recognizes the radar signals using a Softmax classifier based on the fused features from two channels. The experimental results from 12 types of LPI radar signals prove the superiority and robustness of the proposed model. Its overall recognition rate reaches 97% when the signal-to-noise ratio is −6 dB.

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WAN T , JIANG K L , LIAO J Y , et al . Detection and recognition of LPI radar signals using visibility graphs [J ] . Journal of Systems Engineering and Electronics , 2020 , 31 ( 6 ): 1186 - 1192 . DOI: 10.23919/JSEE.5971804 http://doi.org/10.23919/JSEE.5971804 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5971804 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5971804

王红卫 , 董鹏宇 , 陈游 , 等 . 半监督条件下基于朴素贝叶斯和Choi-Williams时频分布能量积累的雷达信号识别 [J ] . 电子与信息学报 , 2021 , 43 ( 3 ): 589 - 597 .

WANG H W , DONG P Y , CHEN Y , et al . Recognition method of radar signal based on the energy cumulant of Choi-Williams distribution and improved semi-supervised Naïve Bayes [J ] . Journal of Electronics & Information Technology , 2021 , 43 ( 3 ): 589 - 597 . (in Chinese)

LIU S K , YAN X P , LI P , et al . Radar emitter recognition based on SIFT position and scale features [J ] . IEEE Transactions on Circuits and Systems II: Express Briefs , 65 ( 12 ): 2062 - 2066 . DOI: 10.1109/TCSII.2018.2819666 http://doi.org/10.1109/TCSII.2018.2819666 https://ieeexplore.ieee.org/document/8326561/ https://ieeexplore.ieee.org/document/8326561/

王星 , 王俊迪 , 金政芝 , 等 . 机载雷达告警接收机发展现状及趋势 [J ] . 雷达学报 , 2023 , 12 ( 2 ): 376 - 388 .

WANG X , WANG J D , JIN Z Z , et al . Current situation and development demands for a radar warning receiver system [J ] . Journal of Radars , 2023 , 12 ( 2 ): 376 - 388 . (in Chinese)

郭立民 , 寇韵涵 , 陈涛 , 等 . 基于栈式稀疏自编码器的低信噪比下低截获概率雷达信号调制类型识别 [J ] . 电子与信息学报 , 2018 , 40 ( 4 ): 875 - 881 .

GUO L M , KOU Y H , CHEN T , et al . Low probability of intercept radar signal recognition based on stacked sparse auto-encoder [J ] . Journal of Electronics & Information Technology , 2018 , 40 ( 4 ): 875 - 881 . (in Chinese)

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 http://doi.org/10.1109/LCOMM.2021.3084043 https://ieeexplore.ieee.org/document/9440955/ https://ieeexplore.ieee.org/document/9440955/

呙鹏程 , 吴礼洋 . 融合卷积特征与判别字典学习的低截获概率雷达信号识别 [J ] . 兵工学报 , 2019 , 40 ( 9 ): 1881 - 1889 . DOI: 10.3969/j.issn.1000-1093.2019.09.013 http://doi.org/10.3969/j.issn.1000-1093.2019.09.013 针对低截获雷达信号通常采用人工特征选择，且在低信噪比、样本数量少情况下识别率低的问题，提出一种融合雷达信号时频图像的卷积特征与字典学习识别算法。该算法以表征信号调制方式的时频图像为基础，通过时频变换获得信号的二维时频数据，输入到LeNet-5卷积神经网络中。网络通过美国MNIST数据库手写数据集进行预训练，将预训练后网络中的2～6层网络参数迁移到新的LeNet-5中，取出第6卷积层的数据作为提取的卷积特征。使用判别字典学习方法进行识别。仿真结果表明：通过预训练处理能够加快网络的收敛与优化，有效提取到每类信号的卷积特征；与文献［4］、文献［24］、文献［25］、文献［26］中4种算法相比，利用判别字典学习能够在样本少、低信噪比情况下取得较高的识别率。

GUO P C , WU L Y . LPI radar signal recognition with convolution feature and discrimination dictionary learning [J ] . Acta Armamentarii , 2019 , 40 ( 9 ): 1881 - 1889 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2019.09.013 http://doi.org/10.3969/j.issn.1000-1093.2019.09.013 The selection of artificial features, low signal-to-noise ratio and small number of samples lead to low recognition rate for low probability of intercepting radar signal. A recognition algorithm with convolution feature and discrimination dictionary learning is proposed. The proposed algorithm is based on the time-frequency image representing a signal modulation type, and a two-dimensional signal is obtained by time-frequency transformation, which is input into LeNet-5. The network is retrained through MNIST data set. The network parameters of 2-6 layers are transferred to a new LeNet-5, and the data from the 6th convolution layer is extracted as convolutional feature. Finally, recognition is ended up by discrimination dictionary learning. Simulated results show that the network goes faster in convergence and optimization through pre-training, and can effectively extract the convolution feature of each kind of signal. Higher recognition rate is obtained through discrimination dictionary learning in the condition of low SNR and small samples compared with other algorithms. Key

普运伟 , 刘涛涛 , 郭江 , 等 . 基于卷积神经网络和模糊函数主脊坐标变换的雷达辐射源信号识别 [J ] . 兵工学报 , 2021 , 42 ( 8 ): 1680 - 1689 . DOI: 10.3969/j.issn.1000-1093.2021.08.012 http://doi.org/10.3969/j.issn.1000-1093.2021.08.012 针对人工提取雷达辐射源信号特征耗时长、特征不明显等问题，提出一种基于深度学习卷积神经网络和模糊函数主脊坐标变换的雷达辐射源信号识别方法。该方法通过快速离散分数傅里叶变换提取信号的模糊函数主脊，并将模糊函数主脊极坐标域的二维时频图作为卷积神经网络的输入，实现对不同雷达信号的分选识别。仿真实验结果表明：新方法不仅在信噪比为0 dB以上保持100%的识别率，在-6 dB时识别准确率也稳定在90%以上；相对于传统的雷达信号识别方法和其他深度学习模型识别方法，在识别率和鲁棒性上均有较大提升，具有一定的工程应用价值。

PU Y W , LIU T T , GUO J , et al . Radar emitter signal recognition based on convolutional neural network and coordinate transformation of ambiguity function main ridge [J ] . Acta Armamentarii , 2021 , 42 ( 8 ): 1680 - 1689 . (in Chinese)

李东瑾 , 杨瑞娟 , 董睿杰 . 基于深度时频特征学习的雷达辐射源识别 [J ] . 国防科技大学学报 , 2020 , 42 ( 6 ): 112 - 119 .

LI D J , YANG R J , DONG R J . Radar emitter recognition based on the deep learning of time-frequency feature [J ] . Journal of National University of Defense Technology , 2020 , 42 ( 6 ): 112 - 119 . (in Chinese)

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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 intra-pulse modulation classification [J ] . Journal of Jilin University (Engineering and Technology Edition) , 2021 , 51 ( 4 ): 1454 - 1460 . (in Chinese)

秦鑫 , 黄洁 , 查雄 , 等 . 基于扩张残差网络的雷达辐射源信号识别 [J ] . 电子学报 , 2020 , 48 ( 3 ): 456 - 462 . DOI: 10.3969/j.issn.0372-2112.2020.03.006 http://doi.org/10.3969/j.issn.0372-2112.2020.03.006 针对低信噪比条件下，复杂多类雷达辐射源信号识别存在特征提取困难，识别正确率低的问题，本文提出了一种基于时频分析和扩张残差网络的辐射源信号自动识别方法.首先通过时频分析将信号时域波形转换成二维时频图像以反映信号本质特征；然后进行时频图像预处理以保留时频图像完备信息，适应深度学习模型输入；最后构建扩张残差网络以自动提取信号时频图像特征，实现雷达辐射源信号分类识别.实验结果表明，信噪比为-6dB时，该方法对16类雷达辐射源信号的整体识别正确率能够达到98.2%，对时频图像特征相似的类LFM（Linear Frequency Modulation）信号的整体识别正确率超过95%.本文提供了一种新的雷达辐射源信号智能识别方法，具有较好的工程应用前景.

QIN X , HUANG J , ZHA X , et al . Radar emitter signal recognition based on dilated residual network [J ] . Acta Electronica Sinica , 2020 , 48 ( 3 ): 456 - 462 . (in Chinese) DOI: 10.3969/j.issn.0372-2112.2020.03.006 http://doi.org/10.3969/j.issn.0372-2112.2020.03.006 This paper proposes a radar emitter signal recognition method based on time-frequency analysis and dilated residual network (DRN) to solve the problem of difficulty in feature extraction and low accuracy in recognition of complex multiple radar emitter signals under low signal-to-noise ratio (SNR).Firstly,the signal time-domain waveform is transformed into a two-dimensional time-frequency image by time-frequency analysis to reflect the essential characteristics of signal.Then the time-frequency image pre-processing is carried out to retain the time-frequency image complete information and adapt to the deep learning model input.Finally,the DRN is constructed to automatically extract the signal time-frequency image features and realize the recognition of radar emitter signal.Experimental results show that when the SNR is -6dB,the overall recognition rate of the proposed method for 16 types of radar signals can reach 98.2%,and the overall recognition rate for time-frequency image similar to linear frequency modulation (LFM) signals is more than 95%.In this paper,a new intelligent recognition method for radar emitter signal is presented,which has nice engineering application prospects.

崔邦彦 , 田润澜 , 王东风 , 等 . 基于注意力机制和改进CLDNN的雷达辐射源识别 [J ] . 系统工程与电子技术 , 2021 , 43 ( 5 ): 1224 - 1231 .

CUI B Y , TIAN R L , WANG D F , et al . Radar emitter identification based on attention mechanism and improved CLDNN [J ] . Systems Engineering and Electronics , 2021 , 43 ( 5 ): 1224 - 1231 . (in Chinese)

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董鹏宇 , 王红卫 , 陈游 , 等 . 基于模糊函数主脊切片和深度置信网络的雷达辐射源信号识别 [J ] . 空军工程大学学报(自然科学版) , 2020 , 21 ( 2 ): 84 - 90 .

DONG P Y , WANG H W , CHEN Y , et al . A recognition method of radar emitter signals based on SVD of MRSAF and DBN [J ] . Journal of Air Force Engineering University (Natural Science Edition) , 2020 , 21 ( 2 ): 84 - 90 . (in Chinese)

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)