Feature-Family-Wise Mixture of Experts for Robust Few-Shot Emitter Identification

Abstract

Few-shot specific emitter identification (FS-SEI) is crucial for distinguishing individual transmitters when only limited labeled samples are available. Existing FS-SEI methods often rely heavily on deep feature extraction, while the resulting representations usually lack clear physical interpretability, thereby limiting model transparency and generalization capability. To address this issue, this letter proposes a robust FS-SEI method based on a feature-family-wise mixture of experts (FF-MoE). Unlike existing approaches that rely on implicit feature fusion, the proposed method explicitly decomposes RF representations into physically meaningful feature families and performs adaptive weighting of each expert. This structured fusion improves both generalization and interpretability under limited supervision. Experimental results show that, with only 10 samples per class, the proposed FF-MoE achieves accuracies of 90% and 64% on the WiFi and UAV datasets, respectively. The source code of this work is available on the following GitHub repository: https://github.com/Minyu-Guo/FF-MoE-for-FS-SEI