An expert system for processing uncorrelated satellite tracks.
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Authors
Hecker, Michael A.
Subjects
Artificial Intelligence
Expert Systems
Neural Networks
Orbital Mechanics
Orbital Dynamics
Uncorrelated Tracks
Expert Systems
Neural Networks
Orbital Mechanics
Orbital Dynamics
Uncorrelated Tracks
Advisors
Kanayama, Yutaka
Ross, I.M.
Date of Issue
1992-12
Date
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
Through an array of ground based radar sights and optical cameras, the United
States military tracks objects in near and far Earth orbit. The sensors provide epoch and
ephemeris information that is used to update a database of known objects. While a
majority of the sensor observations are matched to their corresponding satellites, a small
percentage are beyond the capabilities of current software and can not be correlated.
These uncorrelated targets, UCT's, must be manually fitted by orbital analysts in a labor
intensive process. As an alternative to this human intervention, the use of artificial
intelligence techniques to augment the present computer code was explored. Specifically,
an expert system for processing UCT's at the Naval Space Surveillance Command was
developed. Rules were generated through traditional knowledge engineering methods and
by a novel application of machine learning. The initial results are very good with the
operational portions of the system matching the performance of the experts with an
accuracy of 99%. Although not yet complete, the code developed in this research
definitely shows the potential of using artificial intelligence to process UCT's.
Type
Thesis
Description
Series/Report No
Department
Computer Science
Organization
Naval Postgraduate School
Identifiers
NPS Report Number
Sponsors
Funder
Format
232 p.;28 cm.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.