Research on modulation recognition method of electromagnetic signal based on wavelet transform convolutional neural network

Abstract

The method of electromagnetic signal modulation recognition based on wavelet transform convolutional neural network is studied to improve the effect of electromagnetic signal modulation recognition. By analyzing the electromagnetic signal modulation model, the original electromagnetic signal is preprocessed by wavelet transform to remove the noise of the original electromagnetic signal. The processed electromagnetic signal is used as the input of convolutional neural network, and the electromagnetic signal feature vector is extracted through the convolution layer of convolutional neural network. By using full connection operation, the advanced feature vector of electromagnetic signal is integrated, and the electromagnetic signal is classified by softmax function, and the electromagnetic signal modulation recognition result is output, thus realizing the electromagnetic signal modulation recognition. The experimental results show that when the number of layers of wavelet decomposition is 7 and the wavelet function is Db9, the wavelet transform has the best denoising effect on electromagnetic signal data. At the same time, the network training efficiency of this method is high, and the accuracy of electromagnetic signal modulation recognition is as high as 97.2 %, which improves the effect of electromagnetic signal modulation recognition and is suitable for various types of electromagnetic signal modulation recognition.