An expert system for processing uncorrelated satellite tracks.

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.

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Monterey, California. Naval Postgraduate School

Year dc:date.issued

1992

Author dc:creator

• Hecker, Michael A.

Advisors dc:contributor.advisor

• Kanayama, Yutaka
• Ross, I.M.

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/10945/23894

OAI identifier oai:identifier

oai:calhoun.nps.edu:10945/23894