Obtain confidence interval for the machine learning approach to improve orbit prediction accuracy

Hao Peng, Xiaoli Bai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

A machine learning (ML) approach has been recently proposed to improve orbit prediction accuracy of resident space objects through learning from historical data. Previous results have shown that the ML approach can successfully improve the point estimation accuracy. This paper extends the ML approach by introducing the Gaussian process regression (GPR) method to generate uncertainty information about the point estimate. Numerical results demonstrate that GPR can effectively improve the orbit prediction accuracy and the generated uncertainty boundaries can cover the majority of the testing data. Additionally, effects of the number of the basis function used by the GPR and the orbital measurement noise are explored. Results reveal that properly designed and trained GPRs have stable performance for all experiment cases.

Original languageEnglish (US)
Title of host publicationAAS/AIAA Astrodynamics Specialist Conference, 2018
EditorsPuneet Singla, Ryan M. Weisman, Belinda G. Marchand, Brandon A. Jones
PublisherUnivelt Inc.
Pages2131-2147
Number of pages17
ISBN (Print)9780877036579
StatePublished - 2018
EventAAS/AIAA Astrodynamics Specialist Conference, 2018 - Snowbird, United States
Duration: Aug 19 2018Aug 23 2018

Publication series

NameAdvances in the Astronautical Sciences
Volume167

Conference

ConferenceAAS/AIAA Astrodynamics Specialist Conference, 2018
CountryUnited States
CitySnowbird
Period8/19/188/23/18

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Space and Planetary Science

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