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何遵文 , 侯帅 , 张万成 , 等 . 通信特定辐射源识别的多特征融合分类方法 [J ] . 通信学报 , 2021 , 42 ( 2 ): 103 - 112 . DOI: 10.11959/j.issn.1000-436x.2021028 http://doi.org/10.11959/j.issn.1000-436x.2021028 针对通信辐射源个体识别问题，提出了一种基于多通道变换投影、集成深度学习和生成对抗网络的融合分类方法。首先，通过对原始信号进行多种变换得到三维特征图像，据此构建信号的时频域投影以构建特征数据集，并使用生成对抗网络对数据集进行扩充。然后，设计了一种基于多特征融合的双阶段识别分类方法，利用神经网络初级分类器分别对3类特征数据集进行学习，得到初始分类结果。最后，通过叠加融合学习初始分类结果，得到最终的分类结果。实测数据分析结果证明，所提方法相比基于单一特征提取方法和经典多特征提取方法有更高的准确率，使用室外典型场景多径衰落信道模型对辐射源信号进行了处理，所提模型仍可进行有效识别，能够适用于复杂无线信道环境的应用。

HE Z W , HOU S , ZHANG W C , et al. Multi-feature fusion classification method for communication specific emitter identification [J ] . Journal on Communications , 2021 , 42 ( 2 ): 103 - 112 . (in Chinese) DOI: 10.11959/j.issn.1000-436x.2021028 http://doi.org/10.11959/j.issn.1000-436x.2021028 A multi-feature fusion classification method based on multi-channel transform projection, integrated deep learning and generative adversarial network (GAN) was proposed for communication specific emitter identification.First, three-dimensional feature images were obtained by performing various transformations, the time and frequency domain projection of the signal was constructed to construct the feature datasets.GAN was used to expand the datasets.Then, a two-stage recognition and classification method based on multi-feature fusion was designed.Deep neural networks were used to learn the three feature datasets, and the initial classification results were obtained.Finally, through fusion and re-learning of the initial classification result, the final classification result was obtained.Based on the measurement and analysis of the actual signals, the experimental results show that the method has higher accuracy than the single feature extraction method.The multipath fading channel has been used to simulate the outdoor propagation environment, and the method has certain generalization performance to adapt to the complex wireless channel environments.

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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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