Radar Signal Recognition Based on CWD Spectrogram and Inception Module

Abstract

A convolutional neural network (CNN) based on the Choi–Williams Distribution (CWD) spectrogram and inception module is proposed for radar signal recognition. It is used in a complex electromagnetic environment where the traditional methods have a low recognition rate. The method can automatically identify the modulation type of the received signals while ensuring the multiplicity of extracted features and the precision of signal classification. First, the eight radar signals generated in this research are converted into time–frequency images (TFIs) by CWD. Then the time–frequency information of the signals is extracted by the CNN; two asymmetric four-channel structures are then constructed by Inception structure, and the extracted features are fused separately to achieve the purpose of extracting features in both depth and width of the network. Finally, the classification and recognition of radar signals are realized according to the fused feature map. The experimental results prove that the method has excellent performance in terms of recognition rate and noise immunity compared with the traditional recognition methods.