6,270 research outputs found
Semi-Supervised Specific Emitter Identification Method Using Metric-Adversarial Training
Specific emitter identification (SEI) plays an increasingly crucial and
potential role in both military and civilian scenarios. It refers to a process
to discriminate individual emitters from each other by analyzing extracted
characteristics from given radio signals. Deep learning (DL) and deep neural
networks (DNNs) can learn the hidden features of data and build the classifier
automatically for decision making, which have been widely used in the SEI
research. Considering the insufficiently labeled training samples and large
unlabeled training samples, semi-supervised learning-based SEI (SS-SEI) methods
have been proposed. However, there are few SS-SEI methods focusing on
extracting the discriminative and generalized semantic features of radio
signals. In this paper, we propose an SS-SEI method using metric-adversarial
training (MAT). Specifically, pseudo labels are innovatively introduced into
metric learning to enable semi-supervised metric learning (SSML), and an
objective function alternatively regularized by SSML and virtual adversarial
training (VAT) is designed to extract discriminative and generalized semantic
features of radio signals. The proposed MAT-based SS-SEI method is evaluated on
an open-source large-scale real-world automatic-dependent
surveillance-broadcast (ADS-B) dataset and WiFi dataset and is compared with
state-of-the-art methods. The simulation results show that the proposed method
achieves better identification performance than existing state-of-the-art
methods. Specifically, when the ratio of the number of labeled training samples
to the number of all training samples is 10\%, the identification accuracy is
84.80\% under the ADS-B dataset and 80.70\% under the WiFi dataset. Our code
can be downloaded from https://github.com/lovelymimola/MAT-based-SS-SEI.Comment: 12 pages, 5 figures, Journa
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