Uncertainty characterization and surrogate modeling for angles only initial orbit determination

David Schwab, Puneet Singla, Joseph Raquepas

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

Abstract

Initial orbit determination may be used to initialize object tracking and associate observations with a tracked satellite, but only if uncertainty information exists for the approximated orbit. While classical initial orbit determination algorithms only provide a point solution, uncertainty information may be inferred using deterministic sampling techniques. Along with uncertainty characterization, two statistical learning techniques are tested in their ability to approximate the orbit determination mapping: first, a polynomial approximation built from the statistical moments in the state space and second, Gaussian Process Regression.

Original languageEnglish (US)
Title of host publicationAAS/AIAA Astrodynamics Specialist Conference, 2019
EditorsKenneth R. Horneman, Christopher Scott, Brian W. Hansen, Islam I. Hussein
PublisherUnivelt Inc.
Pages3599-3616
Number of pages18
ISBN (Print)9780877036654
StatePublished - 2020
EventAAS/AIAA Astrodynamics Specialist Conference, 2019 - Portland, United States
Duration: Aug 11 2019Aug 15 2019

Publication series

NameAdvances in the Astronautical Sciences
Volume171
ISSN (Print)0065-3438

Conference

ConferenceAAS/AIAA Astrodynamics Specialist Conference, 2019
CountryUnited States
CityPortland
Period8/11/198/15/19

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Space and Planetary Science

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