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

机译:优化两行元素集的过滤以提高GTO对象的重入预测精度

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Predicting re-entry epoch of space objects enables managing the risk to ground population. Predictions are particularly difficult for objects in highly-elliptical 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. (C) 2018 COSPAR. Published by Elsevier Ltd. All rights reserved.
机译:预测空间物体的重新进入时期可以管理对地面人口的风险。对于高椭圆轨道上的物体,预测尤其困难,而对于具有可重入的分量的物体(例如,重载),预测则非常重要。火箭弹(R / Bs)。本文提出了一种方法来过滤两行元素集(TLE),以促进此类对象的准确重入预测。突出显示了使用TLE进行精确分析的困难,并开发了一组可识别错误元素集的过滤器。过滤器设置使用人工生成的TLE时间序列进行了优化。通过分析示例R / B的自动发现的异常值,可以在真实TLE上验证优化结果。根据对96次历史重入的研究,表明对给定分析中使用的所有轨道要素都必须进行TLE滤波,以避免产生相当不准确的结果。 (C)2018年COSPAR。由Elsevier Ltd.出版。保留所有权利。

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