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Optimising filtering of two-line element sets to increase re-entry prediction accuracy for GTO objects

Lidtke, Aleksander A., Gondelach, David J. and Armellin, Roberto (2019) Optimising filtering of two-line element sets to increase re-entry prediction accuracy for GTO objects Advances in Space Research, 63 (3). pp. 1289-1317.

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Abstract

Predicting re-entry epoch of space objects enables managing the risk to ground population. Predictions are particularly difficult for objects in highlyelliptical orbits, and important for objects with components that can survive re-entry, e.g. rocket bodies (R/Bs). This paper presents a methodology to filter two-line element sets (TLEs) to facilitate accurate re-entry prediction of such objects. Difficulties in using TLEs for precise analyses are highlighted and a set of filters that identifies erroneous element sets is developed. The filter settings are optimised using an artificially generated TLE time series. Optimisation results are verified on real TLEs by analysing the automatically found outliers for exemplar R/Bs. Based on a study of 96 historical re-entries, it is shown that TLE filtering is necessary on all orbital elements that are being used in a given analysis in order to avoid considerably inaccurate results.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Lidtke, Aleksander A.
Gondelach, David J.d.gondelach@surrey.ac.uk
Armellin, Robertor.armellin@surrey.ac.uk
Date : 1 February 2019
Funders : European Space Agency
DOI : 10.1016/j.asr.2018.10.018
Grant Title : Technology for Improving Re-Entry Predictions of European Upper Stages through Dedicated Observations
Copyright Disclaimer : © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords : TLE; Two-line element set; Filtering; Outliers; Optimisation; Re-entry prediction; GTO; Geostationary transfer orbit
Depositing User : Clive Harris
Date Deposited : 29 Oct 2018 09:21
Last Modified : 10 Jun 2019 15:05
URI: http://epubs.surrey.ac.uk/id/eprint/849788

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