Specific Emitter Identification by Edge Pattern Detection and Incremental Open-World Learning

Specific emitter identification (SEI) refers to the technique of identifying different individuals from the signals emitted by wireless devices. Recent studies have focused mainly on deep learning (DL) models that automatically learn valid inherent features from raw time-domain signals. However, current studies rarely consider real open-world scenarios, where new classes may emerge during the inference phase, and the utilized model must evolve as new classes incrementally appear. An incremental open-world learning (IOWL) framework is proposed in this paper, and we show how IOWL can continually recognize and learn new classes. The proposed method is based on a novel exemplar selection and generalization mechanism. First, by applying edge pattern detection (EPD) and shifting edge samples along the adversarial direction, a high-quality pseudo unknown dataset is generated to improve the open-set recognition (OSR) process. Second, a hybrid class-incremental learning method is proposed to maintain the previous identification capabilities through boundary exemplar generation, which not only benefits each individual paradigm but also highlights their synergies in a common framework. We provide a theoretical analysis of the obtained generalization error bounds to prove the benefits of the proposed method. Numerical results on real collected data indicate that IOWL consistently outperforms the other baseline algorithms