Improving Thermospheric Density Predictions in Low‐Earth Orbit With Machine Learning

Improving Thermospheric Density Predictions in Low‐Earth Orbit With Machine Learning

Abstract Thermospheric density is one of the main sources of uncertainty in the estimation of satellites' position and velocity in low‐Earth orbit. This has negative consequences in several space domains, including space traffic management, collision avoidance, re‐entry predictions, orbital lif...

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Bibliographic Details
Main Authors: Giacomo Acciarini, Edward Brown, Tom Berger, Madhulika Guhathakurta, James Parr, Christopher Bridges, Atılım Güneş Baydin
Format: Article
Language:English
Published: Wiley 2024-02-01
Series:Space Weather
Subjects:
thermospheric density
satellite drag
machine learning
space weather
space traffic management
space situational awareness
Online Access:https://doi.org/10.1029/2023SW003652

Internet

https://doi.org/10.1029/2023SW003652

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