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

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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Bibliographic Details
Main Authors: Amir Kazemi, Salar Basiri, Volodymyr Kindratenko, Srinivasa Salapaka
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Machine Learning with Applications
Subjects:
Low-data regimes
Wasserstein metric
Synthetic data
Data Augmentation
Extrapolation
Online Access:http://www.sciencedirect.com/science/article/pii/S2666827025000210
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