An end-to-end radar pulse deinterleaving structure based on point cloud mapping

Radar pulse deinterleaving is a critical technology of electronic reconnaissance equipment. This paper proposes an end-to-end radar pulses deinterleaving structure based on point cloud mapping. The core idea is mapping radar pulse description word (PDW) to a point cloud for mimetic vision, which converts the radar pulse deinterleaving task into a point cloud segmentation task. This structure is characterized by lightweight and strong generalization compared to the image segmentation-based deinterleaving structure. Then this paper proposes a multi-stage graph convolution network (MSGCN) based on graph convolution for point cloud segmentation, which utilises the message passing mechanism of the graph structure to effectively extract, pass and fuse the features of different pulses, thus achieving better segmentation performance. The simulation experimental results show that the proposed method can effectively realize the deinterleaving of densely interleaved and overlapped pulses, and the method has an excellent robustness in pulse missing and spurious pulse interference scenarios.