A machine learning and feature engineering approach for the prediction of the uncontrolled re-entry of space objectsThe continuously growing number of objects orbiting around the Earth is
expected to be accompanied by an increasing frequency of objects re-entering
the Earth's atmosphere. Many of these re-entries will be uncontrolled, making
their prediction challenging and subject to several uncertainties.
Traditionally, re-entry predictions are based on the propagation of the
object's dynamics using state-of-the-art modelling techniques for the forces
acting on the object. However, modelling errors, particularly related to the
prediction of atmospheric drag may result in poor prediction accuracies. In
this context, we explore the possibility to perform a paradigm shift, from a
physics-based approach to a data-driven approach. To this aim, we present the
development of a deep learning model for the re-entry prediction of
uncontrolled objects in Low Earth Orbit (LEO). The model is based on a modified
version of the Sequence-to-Sequence architecture and is trained on the average
altitude profile as derived from a set of Two-Line Element (TLE) data of over
400 bodies. The novelty of the work consists in introducing in the deep
learning model, alongside the average altitude, three new input features: a
drag-like coefficient (B*), the average solar index, and the area-to-mass ratio
of the object. The developed model is tested on a set of objects studied in the
Inter-Agency Space Debris Coordination Committee (IADC) campaigns. The results
show that the best performances are obtained on bodies characterised by the
same drag-like coefficient and eccentricity distribution as the training set.
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