One-shot generative distribution matching for augmented RF-based UAV identification

This work addresses the challenge of identifying Unmanned Aerial Vehicles (UAV) using radiofrequency (RF) fingerprinting in limited RF environments. The complexity and variability of RF signals, influenced by environmental interference and hardware imperfections, often render traditional RF-based id...

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Vydané v:	Machine learning with applications Ročník 20; s. 100638
Hlavní autori:	Kazemi, Amir, Basiri, Salar, Kindratenko, Volodymyr, Salapaka, Srinivasa
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.06.2025 Elsevier
Predmet:	Data Augmentation Extrapolation Low-data regimes Synthetic data Wasserstein metric Extrapolation Data Augmentation Low-data regimes Wasserstein metric Synthetic data
ISSN:	2666-8270, 2666-8270
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