SatTransformer: Spectrum Features-Based Identification of LEO Satellites using Transformer

In recent years, the popularity of LEO satellite internet has made satellite security the focus of industry and academia, such as jamming and spoofing attacks aimed at physical satellite signals. Therefore, developing sophisticated anti-jamming and anti-spoofing technologies is essential, and individual identification of satellites is a prerequisite for implementing this technology. However, off-the-shelf LEO system signals exhibit significant differences in fading characteristics, communication protocols, modulation schemes, and Doppler shifts compared to terrestrial signals, which challenges current individual identification. Besides, individual identification research on off-the-shelf LEO systems such as Starlink remains relatively scarce. This paper proposes SatTransformer, a novel satellite signal individual identification method for LEO system signals, integrating nonlinear mapping and Vision Transformer techniques. We enhanced signal spectrum and doppler shift features by nonlinear transform in our identification method, while maintaining a balance between local and global feature extraction. To evaluate the model's performance, we conducted an extensive data collection campaign, acquiring signals from Starlink satellites over a 25-day period, resulting in a dataset comprising over 30,000 data samples. Experimental results demonstrate that our proposed method achieves superior accuracy (91.3%) compared to existing approaches in the field of satellite signal identification.