A Robust Open-Set Specific Emitter Identification for Complex Signals With Class-Irrelevant Features
Zinan Zhou, Guangyu Li, Teng Wang, Deguo Zeng, Xuanpeng Li, Qing Wang · IEEE Transactions on Information Forensics and Security · 2025
Specific Emitter Identification (SEI) is an emitter recognition technology based on the Radio Frequency Fingerprint (RFF) of hardware. The emergence of unknown emitters is frequent in non-cooperative environments, and Open-set Specific Emitter Identification (OSSEI) based studies are becoming increasingly critical. Besides, Radio Frequency (RF) signals contain a substantial number of features that are irrelevant to emitter categories, as a result the distributions of signals are extremely sparse in the feature space. Existing OSSEI methods cannot be capable in extracting categorical representations for such signals, which may lead to wrong recognition. In this work, based on a set of Variational Auto-Encoder (VAE) models, we propose a robust OSSEI framework designed to handle class-irrelevant features. Specifically, we first use the proposed class-independent VAEs to construct categorical representations for each emitter, leveraging signal distributions in the feature space. In addition, to enhance the distinction among inter-class representations and constrain intra-class distributions, we design a supervised contrastive learning (SupCL) based method that generates positive samples for data augmentation by means of sampling from the corresponding distributions. Furthermore, we calculate the category affiliation of signals by integrating reconstruction probabilities and statistical representation features, facilitating the identification of both known and unknown emitters. Finally, we validate the effectiveness of our method from both theoretical and experimental perspectives, achieving state-of-the-art (SOTA) performance.