Unsupervised Specific Emitter Identification Method Using Radio-Frequency Fingerprint Embedded InfoGAN

Abstract

Machine learning approaches are becoming increasingly popular to improve the efficiency of specific emitter identification (SEI). However, in most non-cooperative SEI scenarios, supervised and semi-supervised learning approaches are often incompatible due to the lack of labeled datasets. To solve this challenge, an unsupervised SEI framework is proposed based on information maximized generative adversarial networks (InfoGANs) and radio frequency fingerprint embedding (RFFE). To enhance individual discriminability, a gray histogram is first constructed according to the bispectrum extracted from the received signal before being embedded into the proposed framework. In addition to the latent class input and the RFFE, the proposed InfoGAN incorporates a priori statistical characteristics of the wireless propagation channels in the form of a structured multimodal latent vector to further improve the GAN quality. The probabilistic distribution of the bispectrum is derived in closed-form and the convergence of the InfoGAN is analyzed to demonstrate the influence of the RFFE. Numerical results indicate that the proposed framework consistently outperforms state-of-the-art algorithms for unsupervised SEI applications, both in terms of evaluation score and classification accuracy.