CrossRF: A Domain-Invariant Deep Learning Approach for RF Fingerprinting
This addresses a practical problem for drone security applications by reducing performance degradation across RF channels, though it appears to be an incremental improvement using adversarial learning on existing datasets.
The paper tackles the problem of cross-channel RF fingerprinting for drone identification, where conventional methods suffer from performance degradation across different transmission channels. The proposed CrossRF approach achieves up to 99.03% accuracy when adapting between channels, compared to only 26.39% with conventional methods.
Radio Frequency (RF) fingerprinting offers a promising approach for drone identification and security, although it suffers from significant performance degradation when operating on different transmission channels. This paper presents CrossRF, a domain-invariant deep learning approach that addresses the problem of cross-channel RF fingerprinting for Unmanned Aerial Vehicle (UAV) identification. Our approach aims to minimize the domain gap between different RF channels by using adversarial learning to train a more robust model that maintains consistent identification performance despite channel variations. We validate our approach using the UAVSig dataset, comprising real-world over-the-air RF signals from identical drone models operating across several frequency channels, ensuring that the findings correspond to real-world scenarios. The experimental results show CrossRF's efficiency, achieving up to 99.03% accuracy when adapting from Channel 3 to Channel 4, compared to only 26.39% using conventional methods. The model maintains robust performance in more difficult multi-channel scenarios (87.57% accuracy adapting from Channels 1,3 to 2,4) and achieves 89.45% accuracy with 0.9 precision for controller classification. These results confirm CrossRF's ability to significantly reduce performance degradation due to cross-channel variations while maintaining high identification accuracy with minimal training data requirements, making it particularly suitable for practical drone security applications.