Few-shot Learning and Data Augmentation for Cross-Domain UAV Fingerprinting

In this paper, we propose a novel approach to cross-domain unmanned aerial vehicle (UAV) authentication using radio frequency (RF) fingerprinting based on prototypical networks (PTNs). UAVs present a unique challenge for RF fingerprinting due to their hovering motion, which creates more diverse signal domains compared to other RF devices like Wi-Fi. This results in a severe domain shift problem, where well-trained models struggle to generalize to unseen domains. To address this issue without incurring significant costs in data collection and model retraining, we employ PTNs, a few-shot learning paradigm that enhances cross-domain performance and system viability. We further improve our method's effectiveness by incorporating fine-tuning with data augmentation, maintaining system viability while improving performance. Comprehensive experimental results demonstrate that our approach significantly mitigates domain shift, achieving up to a 20% improvement in cross-domain accuracy for UAV fingerprinting.