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WU Y X , GUOCE H , LI W . Waveform design for radar and extended target in the environment of electronic warfare [J ] . Journal of Systems Engineering and Electronics , 2018 , 29 ( 1 ): 48 - 57 . DOI: 10.21629/JSEE.2018.01.05 http://doi.org/10.21629/JSEE.2018.01.05 Transmit waveform optimization is critical to radar system performance. There have been a fruit of achievements about waveform design in recent years. However, most of the existing methods are based on the assumption that radar is smart and the target is dumb, which is not always reasonable in the modern electronic warfare. This paper focuses on the waveform design for radar and the extended target in the environment of electronic warfare. Three different countermeasure models between smart radar and dumb target, smart target and dumb radar, smart radar and smart target are proposed. Taking the signal-to-interferenceplus-noise ratio (SINR) as the metric, optimized waveforms for the first two scenarios are achieved by the general water-filling method in the presence of clutter. For the last case, the equilibrium between smart radar and smart target in the presence of clutter is given mathematically and the optimized solution is achieved through a novel two-step water-filling method on the basis of minmax theory. Simulation results under different power constraints show the power allocation strategies of radar and target and the output SINRs are analyzed.

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吴礼洋 , 呙鹏程 , 刘超 , 等 . 基于注意力机制增强残差网络的雷达信号调制类型识别 [J ] . 兵工学报 , 2023 , 44 ( 8 ): 2310 - 2318 . DOI: 10.12382/bgxb.2022.0302 http://doi.org/10.12382/bgxb.2022.0302 针对情报分析中在低信噪比下雷达信号调制样式识别率较低的问题,提出基于注意力机制增强残差网络的雷达调制类型识别算法。利用平滑伪Wigner-Ville分布时频变换的强能量聚集特点将信号调制样式转化为二维时频图像;搭建2层卷积网络和6层残差块的残差网络,并在网络之间穿插加入卷积注意力机制模块,用以增强对特征的关注度,提高特征提取的有效性;将二维时频图像输入到该网络模型中实现调制类型识别。仿真实验结果表明,该算法对6类典型雷达信号调制类型能够有效提取到时频图像的特征,在信噪比0dB以上实现100%的正确率,在信噪比-10dB 下正确率依然能够保持94.2%,具有较强的识别优势。

WU L Y , GUO P C , LIU C , et al. Radar signal modulation type recognition based on attention mechanism enhanced residual networks [J ] . Acta Armamentarii , 2023 , 44 ( 8 ): 2310 - 2318 . (in Chinese) DOI: 10.12382/bgxb.2022.0302 http://doi.org/10.12382/bgxb.2022.0302 To solve the problem of low recognition rate of radar signal modulation type under the condition of low SNR in intelligence analysis, an radar signal modulation type recognition method based on attention mechanism enhanced residual networks is proposed. Firstly, drawing on the strong energy aggregation characteristics of the smooth pseudo Wigner-Ville distribution time-frequency transform, the signal modulation type is transformed into a two-dimensional time-frequency image. Then, residual networks composed of a two-layer convolution network and a six-layer residual block are built, and the convolutional block attention module is inserted between the networks to enhance the attention to features and improve the effectiveness of feature extraction. Finally, two-dimensional time-frequency images are input into the network model to realize modulation type recognition. The simulation results show that this method can effectively extract the feature of time-frequency images for six typical radar signal modulation types, and can achieve 100% accuracy above 0dB Signal-Noise Ratio, and still maintain 94.2% accuracy under -10dB Signal-Noise Ratio.

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陈乾 , 洪征 , 司健鹏 . 融合SENet和Transformer的应用层协议识别方法 [J ] . 计算机科学与探索 , 2024 , 18 ( 3 ): 805 - 817 . DOI: 10.3778/j.issn.1673-9418.2304045 http://doi.org/10.3778/j.issn.1673-9418.2304045 协议识别技术在网络通信和信息安全领域具有至关重要的地位和作用。针对现有基于时空特征的协议识别方法提取协议特征不充分、不全面的问题，提出了一种基于SENet和Transformer的应用层协议识别方法。该方法关注协议数据的时空特征，由加入SENet注意力的残差网络构成的空间特征提取模块和Transformer网络编码器构成的时间提取模块组成。空间特征提取阶段，在残差网络结构中加入SE块获取多个卷积通道间的联系，自适应地为通道分配权重，提取不同通道中更加活跃的协议空间特征；时间特征提取阶段，由基于多头注意力机制的Transformer编码器通过堆叠的方式构建时间特征提取模块，利用输入数据的位置信息全面地获取协议数据的时间特征。通过对更加充足的空间特征和更加全面的时间特征的提取和学习，可以获得更有效的协议识别信息，进而提高协议识别性能。在ISCX2012和CSE_CIC_IDS2018混合数据集上的实验结果表明，所提模型的总体识别准确率达到99.20%，[F1]值达到98.99%，高于对比模型。

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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

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徐桂光 , 王旭东 , 汪飞 , 等 . 基于 SE-ResNeXt 网络的低信噪比 LPI 雷达辐射源信号识别 [J ] . 系统工程与电子技术 , 2022 , 44 ( 12 ): 3676 - 3684 . DOI: 10.12305/j.issn.1001-506X.2022.12.11 http://doi.org/10.12305/j.issn.1001-506X.2022.12.11 针对低信噪比(signal to noise ratio, SNR)低截获概率(low probability of intercept, LPI)雷达脉内波形识别准确率低的问题，提出一种基于时频分析、压缩激励(squeeze excitation, SE)和ResNeXt网络的雷达辐射源信号识别方法。首先通过Choi-Williams分布(Choi-Williams distribution, CWD)获得雷达时域信号的二维时频图像(time-frequency image, TFI)；然后进行TFI预处理降低噪声干扰和频率维的位置分布差异，以适应深度学习网络输入；最后在ResNeXt基础上加入扩张卷积和SE结构提取TFI特征，实现雷达辐射源分类。实验结果表明，SNR低至-8 dB时，该方法对12类常见LPI雷达波形的整体识别准确率依然能达到98.08%。

XU G G , WANG X D , WANG F , et al. LPI radar emitter signals recognition in low SNR based on SE-ResNeXt network [J ] . Systems Engineering and Electronics , 2022 , 44 ( 12 ): 3676 - 3684 . (in Chinese) DOI: 10.12305/j.issn.1001-506X.2022.12.11 http://doi.org/10.12305/j.issn.1001-506X.2022.12.11 Aiming at the problem of low signal to noise ratio (SNR) and low probability of intercept (LPI) radar pulse waveform recognition accuracy, a radar emitter signal recognition method based on time-frequency analysis, squeeze-excitation (SE) and ResNeXt network is proposed. Firstly, the radar time domain signal is transformed into a two-dimensional time-frequency image (TFI) by Choi-Williams distribution (CWD); then, the TFI pre-processing is used to reduce the noise interference and the difference in frequency dimension location distribution, adapting to deep learning network input; finally, the TFI features are extracted by adding dilated convolution and SE structure on the basis of ResNeXt to achieve radar emitter classification. The experimental results show that when the SNR is as low as -8 dB, the overall recognition accuracy of the method for 12 types of common LPI radar waveforms can still reach 98.08%.

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