MAML-Resnet for a Low-Data Approach to Unmanned Aerial Vehicle Identification

Unmanned aerial vehicles (UAVs) have revolutionized various industries, but their unregulated use poses significant security risks, necessitating reliable identification methods. Radio frequency (RF) fingerprinting offers a promising solution by leveraging hardware-specific signal imperfections, yet it faces challenges such as data scarcity and poor adaptation to new classes in low-data scenarios. This paper proposes a MAML-ResNet framework that combines Model-Agnostic Meta-Learning (MAML) with the ResNet-18 architecture to address these challenges. The framework converts raw RF signals into time-frequency diagrams via short-time Fourier transform (STFT) for hierarchical feature extraction by ResNet, while MAML enables rapid adaptation to new UAV classes with few samples through bi-level optimization. Experiments on the DronerFa dataset demonstrate that the proposed method achieves $\mathbf{7 6. 4 9 \%}$ accuracy in 5-way 5-shot tasks, outperforming traditional ResNet (59.74 %) and shallow CNN baselines by over 15 %. Future work may explore multi-domain feature fusion, open-set recognition, and lightweight architectures for real-world deployment.