Space-Object Charging and Its Effect on Orbit Evolution

Paul, Smriti Nandan; Frueh, Carolin

doi:10.2514/1.g002733

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…The main perturbations are caused by Shapiro (1963): Of these perturbing accelerations, all but the gravitational ones depend importantly on the shape and orientation of the orbiting object. Other forces (such as the post-Newtonian relativistic correction (Huang et al 1990), the gravitational attraction by other planets, the Lorentz force (Paul and Frueh 2017), Poynting-Robertson and solar-wind drag (Lhotka et al 2016), the Yarkovsky effect (Murawiecka and Lemaitre 2018) are usually quite small (but not necessarily smaller than some listed) and may be incorporated as necessary for high-precision orbit prediction. The Joint Space Operations Center (JSpOC) maintains an up-to-date SOC of all trackable Earth-orbiting objects, made publicly available in the form of two-line element (TLE) sets.…”

Section: Rso Dynamics and Proper Elementsmentioning

confidence: 99%

An investigation on space debris of unknown origin using proper elements and neural networks

Wu¹,

Rosengren²

2023

Celest Mech Dyn Astron

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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.

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