1 research outputs found
Machine learning to predict the solar flux and geomagnetic indices to model density and Drag in Satellites
In recent years (2000-2021), human-space activities have been increasing
faster than ever. More than 36000 Earth' orbiting objects, all larger than 10
cm, in orbit around the Earth, are currently tracked by the European Space
Agency (ESA). Around 70\% of all cataloged objects are in Low-Earth Orbit
(LEO). Aerodynamic drag provides one of the main sources of perturbations in
this population, gradually decreasing the semi-major axis and period of the LEO
satellites. Usually, an empirical atmosphere model as a function of solar radio
flux and geomagnetic data is used to calculate the orbital decay and lifetimes
of LEO satellites. In this respect, a good forecast for the space weather data
could be a key tool to improve the model of drag. In this work, we propose
using Time Series Forecasting Model to predict the future behavior of the solar
flux and to calculate the atmospheric density, to improve the analytical models
and reduce the drag uncertainty