Contrastive Self-Supervised Clustering for Specific Emitter Identification

Abstract

Specific Emitter Identification (SEI) is crucial for attacking and defending Internet of Things (IoT) devices in untrusted scenarios or battlefield environments. However, existing SEI methods usually require annotation information, which is often unavailable in non-cooperative communications and untrusted scenarios. In this paper, we propose a signal contrastive self-supervised clustering (SCSC) method for unsupervised SEI applications. First, we propose SCSC with 1D fingerprint pyramid feature extractor (1D-FPFE) for obtaining hierarchical subtle features of emitter signals. Then, we propose a bit-pulse selection (BPS) strategy and several signal data augmentation methods. By constructing signal positive and negative instance pairs through data augmentation, our approach generates cluster preference representations in a contrastive self-supervised learning manner. Extensive experimental results based on communication burst emitter dataset show that SCSC achieves an accuracy improvement of about 26% over the current best communication signal clustering algorithm. Moreover, SCSC also exhibits good performance and generalization for 30 emitter clustering and few-shot unlabeled signal clustering.