Lightweight and Efficient Hybrid Network for UAV Identification Using Radio Frequency Fingerprinting

The widespread use of unmanned aerial vehicles brings both convenience and potential security risks, posing significant challenges for accurate UAV identification. To address the limitations of existing deep learning-based radio frequency fingerprinting methods in terms of computational complexity and model adaptability, this paper proposes an innovative hybrid model that combines Convolutional Neural Networks and Transformers to exploit both local and global features of RF signals fully. Our model consists of a Local Block and a Global Block. The Local Block employs Partial Convolution for feature extraction, PointWise Convolution to enhance feature representation, and the Squeeze-and-Excitation module to adaptively emphasize critical features, thereby improving local feature expressiveness. The Global Block comprises Unfold, Super Token Transformer Block, and Fold, which together enable effective modeling of global dependencies through signal unfolding, spatiotemporal transformations, and reconstruction. Experimental results show that under signal-to-noise ratio (SNR) conditions ranging from –5 dB to 20 dB, our method achieves an average recognition accuracy of 96.89%, with powerful performance under low SNR conditions. Furthermore, experiments demonstrate the model’s robustness with limited data. Even with only 1,000 training samples, the model maintains an accuracy of 92.14%. When incorporating Mixup data augmentation, high classification performance is sustained with just 500 training samples. These results highlight the method’s strong adaptability and practical potential in complex environments. Our codes and models are available at https://github.com/Zhoukaijie-hy/hybrid-model.