首页|Machine learning-based approach for ballistic coefficient estimation of resident space objects in LEO
Machine learning-based approach for ballistic coefficient estimation of resident space objects in LEO
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NETL
NSTL
Elsevier
The increasing number of Resident Space Objects poses a serious threat to the safe operation of satellites. Alongside with mitigation policies, it is fundamental to predict the trajectories of such objects which requires the accurate estimation of the physical characteristics that influence their orbits. An important role is played by the area-to-mass ratio, i.e., the ratio between the area exposed to the atmosphere/the Sun and the mass of the space object for the LEO/MEO and GEO region. Current literature proposes several approaches for the estimation of the area-to-mass ratio, ranging from semi-analytical to numerical methods. As regards the latter category, recent studies have focused on classification or regression algorithms for specific types of orbits (e.g., sun-synchronous, geostationary). In this context, this paper proposes a machine learning-based regression approach for the estimation of the ballistic coefficient (i.e., the product between the drag coefficient and the area-to-mass ratio) in Low Earth Orbit, covering a wide set of orbital parameters. Using a synthetic space catalogue, the performance of different types of machine learning techniques is evaluated and compared. A sensitivity analysis is carried out to analyse the effect on performance of the number of trainings, the propagation time, the number of objects of the training data set, and the frequency of the measurements. The applicability of the presented approach is tested and discussed using both real (i.e., based on publicly available catalogues of Two-Line Elements) and synthetic datasets.
Space situational awarenessResident space object characterizationMachine learningBallistic coefficient
N. Cimmino、R. Opromolla、G. Fasano
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Department of Industrial Engineering, University of Naples 'Federico IT', Piazzale Tecchio 80, Naples 80125, Italy
NETL
NSTL
Elsevier
