An investigation on space debris of unknown origin using proper elements and neural networks
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10569_2023_Article_10157.pdf
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8.93 MB
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Checksum (MD5)
7a1683dd5feb3102411f1f6e7d3d9660
Author(s)
Wu, Di
Rosengren, Aaron J.
Date Issued
July 25, 2023
Publisher
Springer Netherlands
Citation
Celestial Mechanics and Dynamical Astronomy. 2023 Jul 25;135(4):44
Version
Final published version
Abstract
Abstract
Proper elements represent a dynamical fingerprint of an object’s inherent state and have been used by small-body taxonomists in characterizing asteroid families. Being linked to the underlying dynamical structure of orbits, Celletti, Pucacco, and Vartolomei have recently adopted these innate orbital parameters for the association of debris from breakup or collision into its parent satellite. Building from this rich astronomical heritage and recent foundations, we introduce an unsupervised learning method—density-based spatial clustering of applications with noise (DBSCAN)—to determine clusters of orbital debris in the space of proper elements. Data is taken from the space-object catalog of trackable Earth-orbiting objects in the form of two-line element sets. Proper elements for debris fragments in low-Earth orbit are computed using an ad hoc numerical scheme, akin to the state-of-the-art Fourier-series-based synthetic method for the asteroid domain. Given the heuristic nature of classical DBSCAN, we investigate the use of neural networks, trained on known families, to augment DBSCAN into a classification problem and apply it to analyst objects of unknown origin.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1007/s10569-023-10157-0
