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

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...

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Bibliographic Details
Published inAerospace Vol. 11; no. 7; p. 551
Main Authors Ferrer, Estel, Ruiz-De-Azua, Joan A., Betorz, Francesc, Escrig, Josep
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2024
Subjects
Cooperation
Datasets
distributed space systems
Earth orbits
Energy consumption
inter-satellite links
Intersatellite communications
Low earth orbit satellites
Low earth orbits
Neural networks
non-terrestrial networks
Polar orbits
Propagation
Real time
Remote sensing
Resource utilization
Satellite communications
Satellites
simplified general perturbations
sun-synchronous orbits
Supervised learning
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Summary: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.
ISSN:2226-4310
2226-4310
DOI:10.3390/aerospace11070551