Satellite Detection and Characterization Using Alternate Transformer Architectures for Spectro–Spatial RF Signal Analysis

With the continuous increase in satellite launches and the growing complexity of RF signals, traditional methods struggle to fully exploit the rich data derived from passive frequency scanning. In this paper, we propose an innovative approach based on Transformer architectures to detect and characterize satellites. Our method leverages the intrinsic capabilities of Transformers to model both local and global dependencies in high-dimensional spectro-spatial images. By alternating Transformer layers along the frequency and spatial axes, our model extracts robust, invariant representations even in highly complex signal environments. The design incorporates attention mechanisms that facilitate the simultaneous separation and reconstruction of spectral and spatial footprints, proving especially effective in scenarios with densely overlapping signals and interference. Experimental results on both synthetic and real-world datasets demonstrate that our approach significantly enhances detection and characterization performance compared to traditional architectures, paving the way for more precise and resilient space situational awareness systems.