2023/05/20 by Rohit Khorana, Khorana, Rohit
Engineering · Physics and Astronomy · #Astro and Planetary Science #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Gamma-ray bursts and supernovae #Image and Video Processing (eess.IV) #Space Satellite Systems and Control #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2305.12286
openalex publication_date 2023/05/20 · openalex created_date 2023/05/24 · openalex updated_date 2026/07/28
Given the critical roles that satellites play in national defense, public safety, and worldwide communications, finding ways to determine satellite trajectories is a crucially important task for improved space situational awareness. However, it is increasingly common for satellites to lose connection to the ground stations with which they communicate due to signal interruptions from the Earth's ionosphere and magnetosphere, among other interferences. In this work, we propose utilizing a computer vision based approach that relies on images of the Earth taken by the satellite in real-time to predict its orbit upon losing contact with ground stations. In contrast with other works, we train neural networks on an image-based dataset and show that the neural networks outperform the de facto standard in orbit determination (the Kalman filter) in the scenario where the satellite has lost connection with its ground-based station. Moreover, our approach does not require a priori knowledge of the satellite's state and it takes into account the external factors influencing the satellite's motion using images taken in real-time.