计算机科学
形势意识
人工神经网络
残余物
协方差
空格(标点符号)
航程(航空)
领域(数学分析)
向量空间
状态空间
人工智能
数据挖掘
算法
统计
数学
数学分析
材料科学
几何学
工程类
复合材料
航空航天工程
操作系统
作者
Hilaire Bizalion,Anteo Guillot,Alexis Petit,Romain Lucken
标识
DOI:10.1016/j.asr.2022.07.006
摘要
Two-line element sets (TLE) released by the 18th Space Control Squadron are the most complete source of information for space situational awareness in the public domain. They are used in a wide range of contexts that include navigation of nanosatellites and ground communication. However, the TLE data are known for having significant bias and errors. Predicting the bias of TLE can be a way to correct them automatically and hence optimize the systems that rely on them at low cost, for instance for reentry calculations and mission analysis. This paper shows the performance of a neural network trained on more than 4500 inactive resident space objects (RSO) on 10 months of orbital data, on all types of orbits. It is shown that the state vector errors are corrected by at least 40% for 70% of the TLE and that the residual error distributions are well described by a Student's t-distribution for which covariance elements are defined consistently. The performance of the trained neural network are shown to be similar for multiple active satellites.
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