Inter-Satellite Link Prediction with Supervised Learning: An Application in Polar Orbits

Abstract

Distributed space systems are increasingly valued in the space industry, as they enhance mission performance through collaborative efforts and resource sharing among multiple heterogeneous satellites. Additionally, enabling autonomous and real-time satellite-to-satellite communications through Inter-Satellite Links (ISLs) can further increase the overall performance by allowing cooperation without relying on ground links and extensive coordination efforts among diverse stakeholders. Given the constrained resources available onboard satellites, a crucial element of achieving cost-effective and autonomous cooperation involves minimizing energy wastage resulting from unsuccessful or unnecessary communication. To address this challenge, satellites must anticipate their ISL opportunities or encounters with minimal resource utilization. Building upon prior publications, this work presents further insights into the use of supervised learning to enable satellites to forecast their encounters without relying on orbit propagation. In particular, a more realistic definition of satellite encounters, along with a versatile solution applicable to all polar low-Earth orbit satellites is implemented. Results show that the trained model can anticipate encounters for realistic and unseen data from an available data source with a balance accuracy of around 90% and six times faster when compared with the well-known Simplified General Perturbation 4 orbital model.