A Self-Supervised-Based Approach of Specific Emitter Identification for the Automatic Identification System

Abstract

Specific Emitter Identification (SEI) is vital for maritime traffic safety in the Automatic Identification System (AIS). Current deep learning SEI methods rely heavily on large amounts of annotated data to learn radio frequency fingerprint (RFF) features, which is challenging to obtain under non-cooperative communication conditions and time-consuming to annotate manually. In this paper, we propose a novel momentum-based asymmetric algorithm called the Contrastive and Non-Contrastive Self-Supervised Learning (CoNSSL) method for few-shot SEI. Specifically, we first perform data augmentation on the emitter signals to construct positive and negative samples. Then, we design an asymmetric dual-network architecture, consisting of an online network and a target network, to map the positive and negative sample pairs into the RFF representation spaces of both networks. A contrastive loss function is employed to maximize the similarity between positive pairs and minimize the similarity between negative pairs. Finally, the RFF representations of positive samples obtained by the online network are introduced into another space and compared with the RFF representations of positive samples from the target network for consistency, further enhancing the learning of robust and generalizable RFF features. Experimental results show that CoNSSL effectively learns universal RFF features on a 50-class unlabeled AIS signal dataset and a 5-class universal software radio peripheral (USRP) dataset. In a 10-shot scenario, CoNSSL achieves recognition accuracies of 93.29% and 78.40%, respectively, with a simple linear classifier, outperforming state-of-the-art Self-Supervised Learning (SSL) SEI methods.