Multi-Classifier Fusion for Open-Set Specific Emitter Identification

Abstract

To safeguard the privacy and security of IoT systems, specific emitter identification is utilized to recognize device identity with hardware characteristics. In view of the growing demand for identifying unknown devices, this paper aims to discuss open-set specific emitter identification. We firstly build up a problem formulation for open-set SEI by discussing the working mechanisms of radio signals and open-set recognition. And then it is pointed out that feature coincidence is an intractable challenge in open-set SEI. The reason, accounting for this phenome, is that pretrained fingerprint feature extractors are incapable of clustering unknown device features and differentiating them from known ones. Considering that feature coincidence leads to error recognition of unknown devices, we propose to fuse multi-classifiers in the decision layer to improve accuracy and recall. Three distinct inputs and four different fusion methods are adopted in this paper to implement multi-classifier fusion. The datasets collected at Huanghua Airport demonstrate that the proposed method can avoid the coincidence of feature space and achieve higher accuracy and recall.