An Automated Space Object Taxonomy of Geostationary Objects

Rochelle N Mellish, Purdue University


Taxonomies are a useful method for providing structure when grouping large numbers of individual near-earth space objects in near-Earth orbits. 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; as such, 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.




Frueh, Purdue University.

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