LPI Radar Signal Recognition Based on CNN-Swing Transformer Network

Aiming at the problem of low recognition accuracy of the low probability of intercept radar signal modulation method under the condition of low signal-to-noise ratio(SNR), a radar signal recognition method based on Transformer and convolutional neural network (CNN) is proposed. First, the Swin Transformer model is introduced and the CNN feature extraction layer is designed at the front end of the model to construct the CNN-Swin transformer network (CSTN). Then the time-frequency characteristics of radar signals are obtained by time-frequency analysis. The images are input into CSTN model for training after image preprocessing, and richer semantic information of images is continuously extracted from the bottom to the top of the network. Finally, six types of signals with different modulation modes are classified and recognized by Softmax classifier. Simulation experiments show that when the SNR is -18 dB, the average recognition rate of the method for six types of typical radar signals reaches 94. 26%, which proves the feasibility of the proposed method.