Prototype Matching with Domain Alignment for Open-world Specific Emitter Identification

Open-world specific emitter identification (SEI) is a practical but challenging task, because it requires accurate identification of both known and unknown emitters in open environments with channel variations. However, traditional closed-set SEI methods suffer from severe performance degradation in open-world environments since 1) closed-set SEI models would misclassify unknown specific emitters (SEs) as known ones, and 2) channel variations would lead to distribution shifts in the radio frequency (RF) fingerprint of known SEs. It is challenging to address both issues, as they coexist and interact with each other. In this paper, we propose a novel prototype matching with domain alignment framework for open-world SEI, which designed to simultaneously address the challenges of unknown SE identification and channel variation. The proposed framework first pretrains a feature extractor and then trains the pretrained feature extractor and an extended open-set classifier via the self-training paradigm. Towards the former, a partial domain alignment strategy is introduced to mitigate the impact of channel variations. Towards the latter, a prototype matching-based pseudo-labeling strategy is proposed to generate reliable pseudo-labels, facilitating subsequent fine-grained identification of unknown SEs. An open-set classification loss is introduced later to help the classifier avoid classifying unknown SEs as known ones. Experiments conducted on a public WiFi dataset demonstrate the superiority and robustness of the proposed method, illustrating its potential application prospects in open-world environments.