Resident space object shape inversion via Adaptive Hamiltonian Markov Chain Monte Carlo

Richard Linares, John L. Crassidis

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

3 Scopus citations

Abstract

This paper presents a method to determine the shape of a space object while simultaneously recovering the observed space object's inertial orientation. This paper employs an Adaptive Hamiltonian Markov Chain Monte Carlo estimation approach, which uses light curve data to infer the space object's orientation, shape, and surface parameters. This method is shown to work well for relatively high dimensions and non-Gaussian distributions of the light curve inversion problem.

Original languageEnglish (US)
Title of host publicationSpaceflight Mechanics 2016
EditorsMartin T. Ozimek, Renato Zanetti, Angela L. Bowes, Ryan P. Russell, Martin T. Ozimek
PublisherUnivelt Inc.
Pages4193-4212
Number of pages20
ISBN (Print)9780877036333
StatePublished - Jan 1 2016
Event26th AAS/AIAA Space Flight Mechanics Meeting, 2016 - Napa, United States
Duration: Feb 14 2016Feb 18 2016

Publication series

NameAdvances in the Astronautical Sciences
Volume158
ISSN (Print)0065-3438

Other

Other26th AAS/AIAA Space Flight Mechanics Meeting, 2016
CountryUnited States
CityNapa
Period2/14/162/18/16

Fingerprint Dive into the research topics of 'Resident space object shape inversion via Adaptive Hamiltonian Markov Chain Monte Carlo'. Together they form a unique fingerprint.

Cite this