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An Automated Taxonomy for Human-Made Objects in Geosynchronous Orbits

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Abstract

Taxonomies are useful for providing structure when categorizing large numbers of near-Earth space objects by the types of maneuvers they perform. In particular, lateral thrusting, longitudinal thrusting, and drifting may be directly linked to detectable changes in the orbital elements that affect object location and orientation. The purpose of this work is to develop a fully-automated taxonomy of the geosynchronous objects based on dynamical principles. Groups of objects are found using clustering methods, and two clustering methods are compared for constructing the taxonomy. The first is an adaptive k-means algorithm that does not require a priori information. It is compared to an agglomerative clustering algorithm that utilizes limits on cluster sizes to form distinct clusters. The effectiveness of the automated taxonomy is determined by comparison with the European Space Agency’s DISCOS database and clusters from the Geosynchronous yearly report.

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Acknowledgements

This article was originally composed while the first author was a graduate student at Purdue University. She gratefully acknowledges the Gates Millennium Scholarship for the academic support that made the writing of this article possible.

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  1. School of Aeronautical and Astronautical Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Rochelle Mellish & Carolin Frueh

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  1. Rochelle Mellish
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  2. Carolin Frueh
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Correspondence to Rochelle Mellish.

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Mellish, R., Frueh, C. An Automated Taxonomy for Human-Made Objects in Geosynchronous Orbits. J Astronaut Sci 68, 480–502 (2021). https://doi.org/10.1007/s40295-021-00259-y

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