赵雄文 , 郭春霞 , 李景春 . 基于高阶累积量和循环谱的信号调制方式混合识别算法 [J ] . 电子与信息学报 , 2016 , 38 ( 3 ): 674 - 680 .

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李东瑾 , 杨瑞娟 , 董睿杰 . 基于联合投影字典学习的辐射源调制识别 [J ] . 兵工学报 , 2020 , 41 ( 7 ): 1464 - 1472 . DOI: 10.3969/j.issn.1000-1093.2020.07.026 http://doi.org/10.3969/j.issn.1000-1093.2020.07.026 针对字典学习用于辐射源识别时原子表征能力有限和复杂环境适应性不足问题，提出一种基于联合投影字典学习的辐射源识别方法。利用时频变换提取辐射源信号初始特征，并通过降维、降噪实现特征预处理；采用核空间投影和降维投影学习方式优化字典原子结构，基于数据集训练获取联合投影字典；通过分类测试完成了有效性验证。仿真结果表明：该方法所提取字典原子具备较强表征能力，能够适应参数多变的复杂环境；较常规有监督字典学习方式更易区分多类型、高相似度信号，-6 dB时单载频信号、线性调频信号、非线性调频信号、二相编码信号、四相编码信号、Frank信号、二相频率编码信号、四相频率编码信号、非线性调频-二相编码复合调制信号、二相频率编码-二相编码复合调制信号10类辐射源信号的整体平均识别率为94.4%.

LI D J , YANG R J , DONG R J . Emitter signal modulation recognition based on joint projection dictionary learning [J ] . Acta Armamentarii , 2020 , 41 ( 7 ): 1464 - 1472 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2020.07.026 http://doi.org/10.3969/j.issn.1000-1093.2020.07.026 An emitter signal recognition method based on joint projection dictionary learning (JPDL) is proposed for the limited atomic representation ability and the insufficient adaptability of complex environment for dictionary learning. The initial features of emitter signal are extracted by time-frequency transform, and the feature preprocessing is realized by dimensionality reduction and noise reduction. Then the atomic structure of the dictionary is optimized by using the methods of kernel space projection and dimensionality reduction projection, and a joint projection dictionary is obtained through data set training. The validity verification is completed by the classification test. The simulated results show that the extracted dictionary atoms have strong representation ability and can adapt to the complex environment with variable parameters. Compared with the conventional supervised dictionary learning method, the proposed method can better distinguish the multi-type and high-similarity signals. The overall recognition rate of 10 types of emitter signals, such as single carrier frequency modulation (SCFM) signal, linear frequency modulation (LFM) signal, nonlinear frequency modulation (NLFM) signal, binary phase shift keying (BPSK) signal, quadrature phase shift keying (QPSK) signal, Frank signal, binary frequency shift keying (BFSK） signal, quadrature frequency shift keying (QFSK) signal, LFM-BPSK modulation signal and BFSK-BPSK modulation signal, at -6 dB is 94.4%. Key

江伟华 , 童峰 , 王彬 , 等 . 采用主分量分析的非合作水声通信信号调制识别 [J ] . 兵工学报 , 2016 , 37 ( 9 ): 1670 - 1676 . DOI: 10.3969/j.issn.1000-1093.2016.09.017 http://doi.org/10.3969/j.issn.1000-1093.2016.09.017 由于信道传输特性、信噪比低等因素的影响，非合作水声通信信号的调制识别极具挑战性。对信号功率谱、平方谱进行主分量分析，提取代表不同类型调制信号特有信息的主分量作为特征参数，从而降低特征参数维度、抑制噪声影响，并在此基础上设计一种基于人工神经网络的水声通信信号调制方式分类器。海上实录信号数据的识别实验结果表明了该方法的有效性。

JIANG W H , TONG F , WANG B , et al . Modulation recognition method of non-cooperation underwater acoustic communication signals using principal component analysis [J ] . Acta Armamentarii , 2016 , 37 ( 9 ): 1670 - 1676 . (in Chinese)

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LIU K , GAO W J , HUANG Q H . Automatic modulation recognition based on a DCN-BiLSTM network [J ] . Sensors , 2021 , 21 ( 5 ): 1577 . DOI: 10.3390/s21051577 http://doi.org/10.3390/s21051577 https://www.mdpi.com/1424-8220/21/5/1577 https://www.mdpi.com/1424-8220/21/5/1577 Automatic modulation recognition (AMR) is a significant technology in noncooperative wireless communication systems. This paper proposes a deep complex network that cascades the bidirectional long short-term memory network (DCN-BiLSTM) for AMR. In view of the fact that the convolution operation of the traditional convolutional neural network (CNN) loses the partial phase information of the modulated signal, resulting in low recognition accuracy, we first apply a deep complex network (DCN) to extract the features of the modulated signal containing phase and amplitude information. Then, we cascade bidirectional long short-term memory (BiLSTM) layers to build a bidirectional long short-term memory model according to the extracted features. The BiLSTM layers can extract the contextual information of signals well and address the long-term dependence problems. Next, we feed the features into a fully connected layer. Finally, a softmax classifier is used to perform classification. Simulation experiments show that the performance of our proposed algorithm is better than that of other neural network recognition algorithms. When the signal-to-noise ratio (SNR) exceeds 4 dB, our model’s recognition rate for the 11 modulation signals can reach 90%.

王洋 , 冯永新 , 宋碧雪 , 等 . DP-DRCnet卷积神经网络信号调制识别算法 [J ] . 兵工学报 , 2023 , 44 ( 2 ): 545 - 555 . DOI: 10.12382/bgxb.2021.0620 http://doi.org/10.12382/bgxb.2021.0620 卷积神经网络在降低系统网络开销的同时,如何保证较高的信号调制识别准确率是目前面临的重要问题。提出一种轻量级卷积神经网络。该网络分为两路,并行提取信号的自相关和互相关特征,之后两路特征进行合并,实现不同调制方式的分类识别;该网络采用控制模型中卷积层的输入数据维度及卷积核数量的方案,实现对网络模型开销的控制。通过对多种不同的调制方式进行识别验证。实验结果表明:在信噪比为-6~12dB条件下,其平均识别准确率可达到86.5%;与传统卷积神经网络相比,计算量降低了94.44%;与常规轻量级卷积神经网络相比,计算量降低了67.6%,该网络性能优于现有的基于轻量级卷积神经网络的调制方式识别方法。

WANG Y , FENG Y X , SONG B X , et al . A modulation recognition algorithm of DP-DRCnet convolutional neural network [J ] . Acta Armamentarii , 2023 , 44 ( 2 ): 545 - 555 . (in Chinese) DOI: 10.12382/bgxb.2021.0620 http://doi.org/10.12382/bgxb.2021.0620 How to ensure higher signal modulation recognition accuracy while reducing system network overhead is a important problem currently faced by the convolutional neural networks.To this end, a lightweight convolutional neural network is proposed.This networkis split into two paths to parallelly extract auto-correlation and cross-correlation features of signal.Then. features from these two paths are combined so that the network can ultimately achieve classification and recognition with different modulation modes. In addition, the overhead of the network is controlled by adopting the scheme of controlling the input data dimension of the convolution layer and the number of convolution cores in the model.The recognition verification of different modulation modes is performed.The experimental result shows that: the average recognition accuracy reaches 86.5% when the signal-to-noise ratio is in the range of -6~12dB;compared with the conventional convolutional neural network, the computational load is reduced by 94.44%; compared with the regular lightweight convolutional neural network, the computational load is reduced by 67.6%.The performance of the proposed network is better than the existing modulation recognition methods based on lightweight convolutional neural network.

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朱克凡 , 王杰贵 , 刘有军 . 小样本条件下基于数据增强和WACGAN的雷达目标识别算法 [J ] . 电子学报 , 2020 , 48 ( 6 ): 1124 - 1131 . DOI: 10.3969/j.issn.0372-2112.2020.06.012 http://doi.org/10.3969/j.issn.0372-2112.2020.06.012 目前小样本条件下高分辨距离像雷达目标识别算法存在识别率较低、识别率稳定度较差等问题，对此，本文提出了基于数据增强和加权辅助分类生成对抗网络（Weighted Auxiliary Classifier Generative Adversarial Networks，WACGAN）的雷达目标识别算法.该算法首先根据雷达目标散射特性，通过时间镜像数据增强方法扩充数据集，然后将扩充数据集输入WACGAN，通过自动选择高质量的生成样本，使判别器在标签样本监督学习的基础上得到进一步优化，最后直接利用判别器实现对雷达目标的有效识别.仿真实验结果表明，本文算法在不增加识别时间的基础上，有效提高了小样本条件下对雷达目标的识别率和识别稳定度.

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