Cross-Domain Few-Shot Specific Emitter Identification via Contrastive Self-Supervised Learning

Specific Emitter Identification (SEI) is an emerging technology that leverages radio frequency fingerprint (RFF) to identify wireless devices. While deep learning methods have shown significant potential for automatic RFF feature extraction and identification, their reliance on large labeled datasets limits their practical applicability. This letter proposes a contrastive self-supervised learning framework integrating von Neumann entropy and exponential moving average (CSEE). With the support of CSEE, an RFF extractor can be pre-trained using unlabeled auxiliary datasets from the source domain. CSEE can directly control the eigenvalue distribution in the final representation’s autocorrelation matrix, thereby optimizing representation properties such as rank and isotropy while reducing information redundancy, thus preserving critical signal-domain features. In the target domain, only a few samples are needed to fine-tune the model to achieve good recognition accuracy. We conducted cross-domain SEI experiments using ADS-B as the source domain with LoRa and AIS as two separate target domains. The results show that with just 30 fine-tuned samples, the CSEE-pretrained RFF extractor achieved nearly 90% identification accuracy, surpassing other self-supervised learning methods. These findings demonstrate the strong generalization capability of CSEE across different target domains. The codes are available at https://github.com/NavLabLqs/CSEE.