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Data association and uncertainty pruning for tracks determined on short arcs

Pirovano, Laura, Principe, Gennaro and Armellin, Roberto (2020) Data association and uncertainty pruning for tracks determined on short arcs Celestial Mechanics and Dynamical Astronomy, 132 (1).

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Abstract

When building a space catalogue, it is necessary to acquire multiple observations of the same object for the estimated state to be considered meaningful. A first concern is then to establish whether different sets of observations belong to the same object, which is the association problem. Due to illumination constraints and adopted observation strategies, small objects may be detected on short arcs, which contain little information about the curvature of the orbit. Thus, a single detection is usually of little value in determining the orbital state due to the very large associated uncertainty. In this work, we propose a method that both recognizes associated observations and sequentially reduces the solution uncertainty when two or more sets of observations are associated. The six-dimensional (6D) association problem is addressed as a cascade of 2D and 4D optimization problems. The performance of the algorithm is assessed using objects in geostationary Earth orbit, with observations spread over short arcs.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Pirovano, Laural.Pirovano@surrey.ac.uk
Principe, Gennaro
Armellin, Roberto
Date : 2 January 2020
DOI : doi.10.1007/s10569-019-9947-8
Copyright Disclaimer : © The Author(s) 2020
Uncontrolled Keywords : Initial orbit determination Data association Space debris
Depositing User : James Marshall
Date Deposited : 29 Jan 2020 15:57
Last Modified : 30 Jan 2020 09:59
URI: http://epubs.surrey.ac.uk/id/eprint/853485

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