Enhancing Specific Emitter Identification Performance in Limited Sample Scenarios Under Digital Predistortion

Specific emitter identification (SEI) is an evolving methodology aimed at discerning individual sources by extracting the radio frequency fingerprint (RFF) inherent within signals. This study confronts the practical challenges associated with digital predistortion (DPD) techniques and insufficient data impacting SEI performance. A novel model is proposed for few-shot SEI (FS-SEI), integrating convolutional neural network (CNN) embedding and metric learning, meticulously tailored to scenarios constrained by limited sample availability. The investigation rigorously examines the ramifications of DPD techniques on SEI, accentuating their potential to erode the salient characteristics defining emitter identities. In the context of SEI with few samples affected by DPD, our methodology begins by representing signals as time-frequency images, followed by the extraction of deep features via a CNN architecture. These extracted features are subsequently embedded within a relational network to assess interfeature relationships. Experimental results substantiate the deleterious impact of DPD techniques on SEI, highlighting the superiority of our proposed approach over traditional CNNs, exhibiting significantly enhanced accuracy in limited sample scenarios.