Date of Award


Document Type


Degree Name

Master of Science in Electrical Engineering


Department of Electrical and Computer Engineering

First Advisor

Stephen C. Cain, PhD.


The US Strategic Command (USSTRATCOM) operated Space Surveillance Network (SSN) is tasked with Space Situational Awareness (SSA) for the US military. This system is made up of Electro-Optic sensors such as the Space Surveillance Telescope (SST) and Ground-based Electro-Optical Deep Space Surveillance (GEODSS) as well as RADAR based sensors such as the Space Fence. While Lockheed Martin’s Space Fence is very adept at detecting & tracking objects in Low Earth Orbit (LEO) below 3000 Km in height [1], gaps remain in the tracking of Resident Space Objects (RSO’s) in Geosynchronous Orbits (GEO) due to limitations associated with the implementation of the SST and GEODSS systems. This thesis explores a reliable, ground-based technique to quickly determine the altitude of a RSO from a single or limited set of observations; implementation of such sensors into the SSN would mitigate GEO SSA performance gaps. The research entails a method to distinguish between the point spread function (PSF) observed by a star and the PSF observed from an RSO using Multi-Hypothesis Testing with parallax as a test criterion. Parallax is the effect that an observed object will appear to shift when viewed from different positions. This effect is explored by generating PSFs from telescope observations of space objects at different baselines. The research has shown the PSF of an RSO can be distinguished from that of a star using single, simultaneous observations from reference and parallax sensing telescopes. This thesis validates these techniques with both simulations and with experimental data from the SST and Naval Observatory sensors.

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