Space object classification using model driven and data driven methods

Richard Linares, John L. Crassidis

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

1 Scopus citations


In recent years there has been an increase in the number of inactive and debris Space Objects (SOs). This work examines both data driven and model driven SO classification. The model driven approach investigated for this work is based on the Multiple Model Adaptive Estimation approach to extract SO characteristics from observations while estimating the probability the observations belonging to a given class of objects. The data driven methods are based on Principle Component Analysis and Convolutional Neural Network Classification approaches. The performance of these strategies for SO classification is demonstrated via simulated scenarios.

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.
Number of pages19
ISBN (Print)9780877036333
StatePublished - 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
ISSN (Print)0065-3438


Other26th AAS/AIAA Space Flight Mechanics Meeting, 2016
Country/TerritoryUnited States


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