Meta learning-based open-set identification system for specific emitter identification in non-cooperative scenarios
Cunxiang Xie, Limin Zhang, Zhaogen Zhong · KSII Transactions on Internet and Information Systems · 2022
The development of wireless communication technology has led to the underutilization of radio spectra.To address this limitation, an intelligent cognitive radio network was developed.Specific emitter identification (SEI) is a key technology in this network.However, in realistic non-cooperative scenarios, the system may detect signal classes beyond those in the training database, and only a few labeled signal samples are available for network training, both of which deteriorate identification performance.To overcome these challenges, a meta-learningbased open-set identification system is proposed for SEI.First, the received signals were preprocessed using bi-spectral analysis and a Radon transform to obtain signal representation vectors, which were then fed into an open-set SEI network.This network consisted of a deep feature extractor and an intrinsic feature memorizer that can detect signals of unknown classes and classify signals of different known classes.The training loss functions and the procedures of the open-set SEI network were then designed for parameter optimization.Considering the few-shot problems of open-set SEI, meta-training loss functions and meta-training procedures that require only a few labeled signal samples were further developed for open-set SEI network training.The experimental results demonstrate that this approach outperforms other state-ofthe-art SEI methods in open-set scenarios.In addition, excellent open-set SEI performance was achieved using at least 50 training signal samples, and effective operation in low signalto-noise ratio (SNR) environments was demonstrated.