Radar Signal Intra-Pulse Modulation Recognition Based on Point Cloud Network

Aiming at the existing deep learning radar signal modulation recognition methods are mostly based on time-frequency image (TFI) and consequently result in networks with a large number of parameters due to the significant amount of redundant information contained in TFI, this paper proposes a radar signal intra-pulse modulation recognition method based on point cloud which removes redundant information. Radar signals of different modulation types are mapped into point cloud after Smoothed Pseudo Wigner-Ville Distribution (SPWVD) transformation. Then, PointNet++ is used to classify the point cloud data according to its modulation type and output its corresponding modulation type labels. Simulation results show that the proposed method can effectively recognize radar signals of typical modulation types, and show strong effectiveness and reliability at low signal-to-noise ratio (SNR). Besides, the lightweight characteristics of PointNet++ make the operation of the method more efficient.