Detecting Low-Orbit Satellites via Adaptive Optics Based on Deep Learning Algorithms

This research proposes the design and implementation of an adaptive optical system (AOS) for monitoring low-orbit satellites (LOSs) to ensure that they do not deviate from their pre-planned path. This is achieved by designing a telescope with an optical system that contains six mirrors in a regular hexagonal shape; the side length of one mirror is 30 cm, and there is also a spectral analyzer system in the middle to separate the spectra emitted by stars from those reflected from low-orbit satellites. A SwinTrack-Tiny (STT) is used, with modifications using temporal information via insertion. The model incorporates a new purpose-built image update template as a third input to the model and combines the attributes of the new image with the attributes of the primary template via an attention block. To maintain the dimensions of the original model and take advantage of its weights, an attention block with four vertices is used.