Authors :
Alish Ketankumar Patel
Volume/Issue :
Volume 11 - 2026, Issue 7 - July
Google Scholar :
https://tinyurl.com/4ptyckcm
Scribd :
https://tinyurl.com/2yujrenn
DOI :
https://doi.org/10.38124/ijisrt/26jul1177
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
Keeping a satellite in the right orbit is no easy feat. It’s constantly influenced by various forces like Earth’s
uneven gravity, air resistance high up in the atmosphere, and even pressure from sun-light. Over time, these factors can
nudge satellites off their intended paths. Nowadays, most satel-lites depend on ground control teams to keep an eye on
their orbits and plan any necessary adjust-ments. This can be a slow, expensive, and tricky process, especially as the
number of satellites in space keeps increasing. This paper outlines a machine learning approach to check for deviation
from the intended orbit of a given satellite con-stellation. The system continuously tracks essen-tial orbital data, such as
position and velocity, to see if a satellite is veering off course. To develop and test this method, we used publicly available
data from Starlink satellites and other spacecraft. Starlink satellites are particularly valuable because they frequently adjust
their orbits and operate in large clusters, providing rich and dynamic data. We trained a machine learning model to
predict if a satellite has left from its constellation. By com-paring this prediction with the actual position of the satellite, the
system can spot when the orbit starts to drift. The findings indicate that blend-ing basic physics with machine learning
can ef-fectively aid in autonomous orbit correction. This approach holds promise for cutting costs, boost-ing efficiency, and
simplifying the management of a growing number of satellites in the future.
References :
- J0shi, V. (2025). Starlink Satellite TLE/CSV dataset.
- Kapoor, K. (2023). Satellite-Orbital-Catalog.
- Lee, T. O. (2025). Station-Keeping for Ellip-tical Inclined Geosynchronous Orbit Satellite Constellation.
- Li, C. (2022). Geostationary Station-Keeping of Electric-Propulsion Satellite Equipped with Robotic Arms.
- Makadiya, J. (2020). SpaceX All Satellites Database.
Keeping a satellite in the right orbit is no easy feat. It’s constantly influenced by various forces like Earth’s
uneven gravity, air resistance high up in the atmosphere, and even pressure from sun-light. Over time, these factors can
nudge satellites off their intended paths. Nowadays, most satel-lites depend on ground control teams to keep an eye on
their orbits and plan any necessary adjust-ments. This can be a slow, expensive, and tricky process, especially as the
number of satellites in space keeps increasing. This paper outlines a machine learning approach to check for deviation
from the intended orbit of a given satellite con-stellation. The system continuously tracks essen-tial orbital data, such as
position and velocity, to see if a satellite is veering off course. To develop and test this method, we used publicly available
data from Starlink satellites and other spacecraft. Starlink satellites are particularly valuable because they frequently adjust
their orbits and operate in large clusters, providing rich and dynamic data. We trained a machine learning model to
predict if a satellite has left from its constellation. By com-paring this prediction with the actual position of the satellite, the
system can spot when the orbit starts to drift. The findings indicate that blend-ing basic physics with machine learning
can ef-fectively aid in autonomous orbit correction. This approach holds promise for cutting costs, boost-ing efficiency, and
simplifying the management of a growing number of satellites in the future.
