Bai Jiansheng, Yao Jinjie, Qi Juncheng, Wang Liming
State Key Lab for Electronic Testing Technology, North University of China, Taiyuan 030051, China.
School of Information and Communication Engineering, North University of China, Taiyuan 030051, China.
Entropy (Basel). 2022 May 15;24(5):700. doi: 10.3390/e24050700.
AMC (automatic modulation classification) plays a vital role in spectrum monitoring and electromagnetic abnormal signal detection. Up to now, few studies have focused on the complementarity between features of different modalities and the importance of the feature fusion mechanism in the AMC method. This paper proposes a dual-modal feature fusion convolutional neural network (DMFF-CNN) for AMC to use the complementarity between different modal features fully. DMFF-CNN uses the gram angular field (GAF) image coding and intelligence quotient (IQ) data combined with CNN. Firstly, the original signal is converted into images by GAF, and the GAF images are used as the input of ResNet50. Secondly, it is converted into IQ data and as the complex value network (CV-CNN) input to extract features. Furthermore, a dual-modal feature fusion mechanism (DMFF) is proposed to fuse the dual-modal features extracted by GAF-ResNet50 and CV-CNN. The fusion feature is used as the input of DMFF-CNN for model training to achieve AMC of multi-type signals. In the evaluation stage, the advantages of the DMFF mechanism proposed in this paper and the accuracy improvement compared with other feature fusion algorithms are discussed. The experiment shows that our method performs better than others, including some state-of-the-art methods, and has superior robustness at a low signal-to-noise ratio (SNR), and the average classification accuracy of the dataset signals reaches 92.1%. The DMFF-CNN proposed in this paper provides a new path for the AMC field.
自动调制分类（AMC）在频谱监测和电磁异常信号检测中起着至关重要的作用。到目前为止，很少有研究关注不同模态特征之间的互补性以及特征融合机制在AMC方法中的重要性。本文提出了一种用于AMC的双模态特征融合卷积神经网络（DMFF-CNN），以充分利用不同模态特征之间的互补性。DMFF-CNN使用格拉姆角场（GAF）图像编码和智商（IQ）数据与卷积神经网络相结合。首先，通过GAF将原始信号转换为图像，并将GAF图像用作ResNet50的输入。其次，将其转换为IQ数据并作为复值网络（CV-CNN）的输入来提取特征。此外，还提出了一种双模态特征融合机制（DMFF），以融合由GAF-ResNet50和CV-CNN提取的双模态特征。融合特征用作DMFF-CNN的输入进行模型训练，以实现多类型信号的AMC。在评估阶段，讨论了本文提出的DMFF机制的优点以及与其他特征融合算法相比在准确性上的提高。实验表明，我们的方法比其他方法表现更好，包括一些最先进的方法，并且在低信噪比（SNR）下具有卓越的鲁棒性，数据集信号的平均分类准确率达到92.1%。本文提出的DMFF-CNN为AMC领域提供了一条新途径。
