Radar Maritime Target Detection via Spatial–Temporal Feature Attention Graph Convolutional Network
Ningyuan Su, Xiaolong Chen, Jian Guan, Yong Huang, Xinghai Wang, Yonghua Xue
Abstract
The research on maritime target detection signals has significant value in various fields. Conventional statistical theory-based target detection methods are limited by the complex sea clutter environment and target characteristics, making it challenging to achieve high-performance detection. In practical scenarios such as maritime observation, the radar observation area is expansive. And the radar beam cannot remain fixed in one direction for prolonged intervals. Consequently, it is not feasible to accumulate multiple pulses within a single azimuth cell. Therefore, extraction of effective features from echo signals is not practical. To address this issue, this paper proposes a maritime target detection method based on the Spatial-Temporal Feature Attention Graph Convolutional Network (STFA-GCN) and radar signal graph data. Firstly, the multi-frame radar signal is converted into graph data to represent spatial-temporal features. Then a STFA-GCN model perform feature extraction and classification on the graph data nodes, realizing target detection in complex sea clutter backgrounds. The proposed method was tested and evaluated using various target datasets, exhibiting superior detection performance and generalization capabilities. On real measured signal test, the proposed method can achieve 0.917 detection probability at false alarm rate of 1.26×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> . While the 3-frame accumulation CACFAR is 0.839 at 1.65×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> .