复杂电磁环境下基于信号时频图像的调制识别

Modulation recognition algorithm based on signal time-frequency images in complex electromagnetic environment

摘要:

为解决调制识别研究中较少考虑到不同信号的特征之间联系性的问题，搭建了卷积神经网络(CNN)来提取信号的彩色时频图对应的特征，并利用时频变换的分析方法，将一维信号处理成彩色时频图，通过卷积神经网络架构提取图像特征；同时为了提升算法在低信噪比下的分类识别准确率，对时频图像的纹理特征进行了特征提取，将提取到的纹理特征与卷积神经网络中提取到的特征进行特征融合。仿真实验结果表明，采用的时频卷积神经网络(TF–CNN)和TF–Resnet网络框架能够达到高精确度信号自动调制识别分类的目的。

Abstract:

In complex communication environment, the connection between the characteristics of different signals is seldom considered in modulation recognition. A Convolutional Neural Network(CNN) is built to extract the characteristics of the time-frequency images of signals. Time-frequency transform is employed to process the one-dimensional signal into images, and image features are extracted through CNN. In order to improve the classification and recognition accuracy of the algorithm under low SNR, the texture features are also extracted from the images, and they are fused with the features extracted from the CNN. The simulation results show that the Time–Frequency Convolution Neural Network(TF–CNN) and TF–Resnet framework can achieve signal automatic modulation recognition and classification.