Robust Few-Shot Specific Emitter Identification Using Multi-View Feature Fusion with Attention

Radio frequency fingerprinting (RFF) presents a promising solution for advancing specific emitter identification (SEI) methods, which are crucial for securing the Internet of Things (IoT). While deep learning (DL)-based SEI approaches have demonstrated strong potential, they heavily depend on large, labeled datasets, which are often difficult to obtain in real-world scenarios. This reliance limits the robustness of existing SEI methods. To overcome this challenge, we propose a robust few-shot SEI (FS-SEI) method leveraging multi-view feature fusion with attention (MFFA). By integrating interpretable signal processing (SP) features with DL features and incorporating an attention mechanism for adaptive multi-view fusion, the proposed approach enhances both identification accuracy and robustness in few-shot scenarios. Experimental results validate the effectiveness of the method, showing consistent robustness under noisy conditions and significant gains in identification accuracy. These findings highlight its strong potential for practical applications in dynamic and challenging environments.