Low-Earth Satellite Orbit Determination Using Deep Convolutional Networks with Satellite Imagery
This addresses a critical issue for satellite operators in national defense and communications by providing an incremental improvement in orbit determination during signal interruptions.
The paper tackles the problem of determining satellite trajectories when communication with ground stations is lost by using a computer vision approach that analyzes real-time Earth images from the satellite. The result shows that neural networks outperform the standard Kalman filter in this scenario, with no requirement for prior knowledge of the satellite's state.
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 $\textit{a priori}$ 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.