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Rogue emitter detection (RED) is a crucial technique to maintain secureinternet of things applications. Existing deep learning-based RED methods havebeen proposed under the friendly environments. However, these methods performunstable under low signal-to-noise ratio (SNR) scenarios. To address thisproblem, we propose a robust RED method, which is a hybrid network of denoisingautoencoder and deep metric learning (DML). Specifically, denoising autoencoderis adopted to mitigate noise interference and then improve its robustness underlow SNR while DML plays an important role to improve the featurediscrimination. Several typical experiments are conducted to evaluate theproposed RED method on an automatic dependent surveillance-Broadcast datasetand an IEEE 802.11 dataset and also to compare it with existing RED methods.Simulation results show that the proposed method achieves better REDperformance and higher noise robustness with more discriminative semanticvectors than existing methods.

Rogue Emitter Detection Using Hybrid Network of Denoising Autoencoder and Deep Metric Learning