Overcoming Labeled Sample Scarcity and Poor Model Portability: A Robust Framework for Specific Emitter Identification

The intelligent identity authentication based on specific emitter identification (SEI) shows great promise in the field of military and civilian. In real-world scenarios, the distribution of labeled and unlabeled samples is highly imbalanced, only a few labeled samples are available. SEI methods based on consistency regularization (CR) have been proven effective in addressing such issue. However, most existing methods suffer from limited portability, in other words, they are only applicable to specific emitters in specific scenarios. In this letter, a robust framework based on revisiting data augmentation strategies and phased training (RDASPT) is proposed. Two data augmentation called hybrid augmentation (HA) and sampling augmentation (SA) are designed to address the issues of scarcity of labeled samples, poor availability of augmented samples and inconsistency of the predicted class distribution. The design of phased training is conducive to mining deep semantic features embedded in labeled and unlabeled samples. When only 5% of the labeled samples are available, the recognition accuracy reaches over 97% on two real-world datasets, which verify the advantages of RDASPT for data-scarce and portable SEI tasks.