Maritime Target Detection Based on Radar Graph Data and Graph Convolutional Network

Abstract

Due to the complex sea clutter environment and target features, the conventional statistical theory-based methods cannot achieve high performance in maritime target detection tasks. Conventional deep learning, such as convolutional neural networks (CNNs)-based target detection methods process each signal sample independently, and the temporal-spatial domain correlation information is seldom used. To achieve full utilization of information contained in radar signals and improve the detection performance, a graph convolutional network (GCN) is considered, which has shown great advantages in graph data processing and has been applied in the field of signal processing. This letter proposed a maritime target detection method based on radar signal graph data and graph convolution. Graph structure data is applied to define the detection units and to represent the temporal and spatial information of detection units. The target detection of the signal corresponding to the nodes is conducted via GCN. Experimental results show that the proposed approach can effectively detect marine targets when the signal-to-noise ratio is above −5 dB and can effectively suppress false alarms in the pure clutter area, which is not adjacent to targets. Compared with the popular used CNN method, e.g., LeNet, the proposed method can achieve higher detection probability with the same given false alarm rate.