Marine Small Floating Target Detection Method Based on Fusion Weight and Graph Dynamic Attention Mechanism
Hongtao Ru, Shuwen Xu, Qi He, Peng‐Lang Shui
Abstract
Sea surface target detection with Graph Neural Networks (GNN) is an emerging method. However, the correlation information of radar returns cannot be efficiently exploited by the conventional Graph Convolutional Network (GCN). Therefore, this paper proposes a small floating target detection method based on graph attention network (GAT) with spatio-temporal correlation of clutter maps, and designs fusion weighting and dynamic attention mechanism for practical problem. First, the dwell radar data is modeled as a graph structure according to its spatio-temporal information. The proposed graph structure allows Doppler spectra of same-type samples to be accumulated separately for sea clutter and target returns. Then, we propose a GAT-based detector and optimize it to create variants: GATv2, GAT-Fused, and GATv2-Fused. These variants aim to reduce sea spike interference and jointly utilize spatio-temporal clutter map information and feature correlation. Both measured and simulated data demonstrate that the proposed attention-based detectors effectively identify marine small floating targets, including out-of-distribution (OOD) detection, outperforming conventional feature-based detectors, the GCN detector, and the pure GAT detector.