Few‐Shot Specific Emitter Identification Using Open‐Set Recognition Classifiers

ABSTRACT

Physical‐layer‐specific emitter identification (SEI) is challenged by limited labelled data and the presence of previously unseen devices in practical deployments. To address these issues, we propose FS‐SA2SEI‐OSR, an open‐set extension of a few‐shot self‐supervised adversarial augmentation SEI framework. The proposed method combines adversarially trained few‐shot representation learning with continuous wavelet transform scalograms and lightweight open‐set recognition decision layers (OpenMax, EVM, OS‐SVM) to enable reliable classification of known emitters while explicitly rejecting unknown ones. Experiments on Wi‐Fi and public datasets (ADS‐B and FIT/CorteXlab) demonstrate a closed‐set accuracy of 89.9% and an open‐set detection rate of 64.0% at a worst‐case openness of 0.6, with only modest computational overhead. These results indicate that FS‐SA2SEI‐OSR provides a practical and scalable solution for few‐shot open‐set SEI in dynamic and resource‐constrained IoT environments.