RF Fingerprinting Identification in Low SNR Scenarios for Automatic Identification System

The explosive growth of maritime vessels imposes high demands on the security of automatic identification system (AIS). Radio frequency fingerprinting identification (RFFI) as a physical-layer authentication method offers a new perspective on security solutions for wireless communication systems. However, RFFI for AIS satellite component suffers from poor performance under low signal-to-noise ratio (SNR) scenarios. To this end, this paper proposes a data-driven RFFI method with high performance and resilience for AIS satellite links. The bivariate variational mode decomposition (VMD) enables adaptive signal decomposition for effective denoising. The raw I/Q samples after bivariate VMD are directly taken as input to the RF fingerprinting feature extraction network without any transformation process. The complex-valued neural network used for feature extraction provides a superior representation capability compared to traditional real-valued neural networks. Furthermore, channel attention mechanisms are embedded in the feature extraction network to shed light on the correlation between channels within the signal. The following integrated spatial attention mechanisms are employed to reveal the correlations between signals. Numerical results show that the proposed RFFI method can achieve over 90.37% accuracy on real-world datasets with SNRs greater than 6 dB, and achieve a balance between complexity and performance.