Date of Award

9-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Department of Electrical and Computer Engineering

First Advisor

Stephen C. Cain, PhD

Abstract

Ground based astronomical imaging is an important method in gaining situational awareness of orbiting and far off objects in space. This method of imaging is accessible to everyone that can look up into the sky, but the accessibility to digital telescope systems allows for more exciting methods of extracting information. A use case for these telescopes is finding nearby objects to larger brighter known objects. The number satellites in low-earth orbit and geosynchronous earth orbit is becoming more congested as these orbits increase in population. Tens of thousands of satellites and debris now exist in this orbit, with the number expected to grow. This drives a need to know the proximity of potential unknown objects to known assets. Using larger telescopes increases the number of photons a camera can collect, giving us the ability to peer further into space or conversely find fainter objects nearby. This paper focuses on the algorithmic methods of image restoration and detection of objects within ground based imagery, seeking to quantify statistical and neural network methods that perform these tasks.

AFIT Designator

AFIT-ENG-DS-24-S-025

Comments

A 12-month embargo was observed for posting this work on AFIT Scholar.

Distribution Statement A, Approved for Public Release. PA case number on file.

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