Small Target Detection in X-Band Sea Clutter Using the Visibility Graph

Abstract

Small target detection in high-resolution sea clutter is one of the important problems in radar target detection. We propose a new detector that adopts the visibility graph (VG) algorithm and the graph convolution neural network (GCN). This detector extracts the graph feature of radar echo networks for target detection after converting the phase information of radar echo into complex networks. Different from the existing detector based on graph connectivity, the VG algorithm does not need radar data preprocessing and it can directly convert the radar data into complex networks by judging whether the series data are visible mutually. We extracted the graph feature that can effectively distinguish the background clutter and target echo by GCN instead of calculating the statistic of graphs manually. Experiments on the IPIX radar datasets show that the proposed method achieves superior performance than the existing detector based on graph connectivity, especially in a short observation time. When the observation time is 0.256 s, the average accuracy of #17, #54, and #320 datasets can reach 84.82%, 94.84%, and 90.59%, respectively.