In the context of neural network-based radar feature extraction and detection methods, single-feature detection approaches exhibit limited capability in distinguishing targets from background in complex environments such as sea clutter. To address this, a Multi-Feature Extraction Network and Graph Fusion Detection Network (MFEn-GFDn) method is proposed, leveraging feature complementarity and enhanced information utilization. MFEn extracts features from various time-frequency maps of radar signals to construct Multi-Feature Graph Data (MFG) for multi-feature graphical representation. Subsequently, GFDn performs fusion detection on MFG containing multi-feature information. By expanding the fea... More >