Date of Award


Document Type


Degree Name

Master of Science


Department of Electrical and Computer Engineering

First Advisor

Stephen C. Cain, PhD.


To date, much effort has been spent on improving the detection of space objects; however, the ability to track and catalogue space objects remains only as good as the ability to determine the object's position and angular rate. The foundation of space situational awareness (SSA) is the ability to detect a space object and to predict its location in the future. In order to accomplish SSA for Geosynchronous Earth Orbit (GEO) space objects, the Defense Advanced Research Projects Agency (DARPA) developed the Space Surveillance Telescope (SST) to enable ground-based, broad-area search, detection and tracking of small GEO objects in space. In general, the SST tracks along the sky at the sidereal rate where the stars will appear to be stationary, while objects within space not moving at this rate will appear to move across the focal array from data frame to data frame. As the time between each frame is known and the position of the detected object can be determined in the focal array, it is possible to measure the angular position and angular rate of a detected object. The two main types of detection algorithms used in this thesis are the binary hypothesis test (BHT) and the multi-hypothesis test (MHT), which both rely on a log-likelihood ratio (LLR); however, the MHT algorithm adds an additional step to correlate nine system point spread functions, or hypotheses, with the image data. These detection algorithms lead to varying degrees of accuracy and precision in determining the position and angular rate for a detected space object. The research within this thesis shows that the MHT algorithm is more accurate and precise than the BHT algorithm.

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