  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
identification methods ineffective. To address these complications, the study
introduces the rigorous use of one-shot generative methods for augmenting
transformed RF signals, offering a significant improvement in UAV
identification. This approach shows promise in low-data regimes, outperforming
deep generative methods like conditional generative adversarial networks (GANs)
and variational auto-encoders (VAEs). The paper provides a theoretical
guarantee for the effectiveness of one-shot generative models in augmenting
limited data, setting a precedent for their application in limited RF
environments. This research contributes to learning techniques in low-data
regime scenarios, which may include atypical complex sequences beyond images
and videos. The code and links to datasets used in this study are available at
https://github.com/amir-kazemi/uav-rf-id.
