Machine Learning techiques applied to space objects uncontrolled re-entry predictions
Creators: name:  A. PANICO 
name:   P. DI LIZIA 
Publisher: name: Proceedings of  the 9th European Conference for Aerospace Sciences. Lille, France, 27 June - 1 July, 2022
Year: 2022
Subjects: SPACE DEBRIS MODELING
REENTRY

description: Over the decades, Space Situational Awareness (SSA) has been growing in importance and public attention as thousands of civil and military assets populate the orbits around the Earth. Their survivability depends primarily on the capacity of detecting, identifying and handling potential risks or threats. The prediction of the re-entry window is still an open issue since current algorithms have limited accuracy for long period estimations, especially for the objects poorly characterized and/or moving on highly elliptical orbits. The main approaches to deal with this problem are based on numerical propagation and conventional statistical regression. The numerical propagation includes all the relevant perturbations: zonal and tesseral harmonics of the geo-potential third body attraction of the moon and the sun, solar radiation pressure, aerodynamic drag and solar activity. The ideal conditions to effectively propagate the object and estimate the re-entry window is to have a deep understanding of its physical properties and to collect several observations to determine the evolution of the orbital parameters over time. Within these conditions, the final window can be found with high accuracy, having slight variations of the epoch only during the last days and a progressive reduction of the uncertainty. Space debris and objects orbiting in highly elliptical orbits may not meet these requirements. Indeed, the characterization of the former depends on the specific genesis, but some features like size, shape, mass and the value of the exposed area of debris are usually unknown and might be estimated with regression techniques through a dedicated observation campaign. However, limited measurements related to any highly elliptical orbiting object are typically acquired. This is especially true when their orbits show significant variations, which make medium-long term predictions inaccurate and consequently the re-entry window usually converges a few orbits before the actual re-entry epoch. The recent developments in Machine Learning (ML) techniques and architectures to deal with regression problems can support the estimation of the re-entry window for these kinds of objects. This work investi- gates the advantages offered by ML architectures in providing an augmented level of awareness on the risk posed by the reentry of these objects in Earths atmosphere. More specifically, several Deep Feed-Forward architectures are studied, along with different meta-parameters and data configurations in order to assess a target baseline with a conventional ML technique. Moreover, additional analyses are carried out to verify if other artificial intelligence approaches might suit better this specific problem, along with combining the solution provided by this statistical technique with conventional propagation based on orbital dynamics. All the data used for this work were downloaded from publicly available sources and a conventional training-validation dataset split was adopted to verify the goodness of the model obtained. The results were compared with official estimations carried out by the USSTRATCOM in the frame of the publicly available space-track service, showing a tendency to reduce the error in medium-long term prediction (i.e., from 15 to 45 days before the real re-entry epoch) with a significant reduction in the computational time. As a result, the next-generation database scanning algorithms will likely take advantage of these ML approaches to provide a first guess of the re-entry window and to trigger a data acquisition process in order to refine the re-entry epoch and location predictions with all the available techniques.