Date of Award

3-21-2019

Document Type

Thesis

Degree Name

Master of Science in Systems Engineering

Department

Department of Systems Engineering and Management

First Advisor

John M. Colombi, PhD

Abstract

Persistent Space Situational Awareness (SSA) is one of the top priorities of the DoD. Currently the Space Surveillance Network (SSN) operates using only a task-based method. The goal of this thesis was to compare the current task-based space surveillance performance to a search-based method of space surveillance in the GEO belt region. The performance of a ground telescope network, similar to the Ground-Based Electro-Optical Deep Space Surveillance (GEODSS) network, was modeled and simulated using AGI’s Systems Tool Kit (STK) and Python. The model compared search-based and task-based space surveillance methods by simulating 813 Resident Space Objects (RSOs) on the summer solstice, fall equinox and winter solstice. Four performance metrics for comparing the search-based and task-based methods were minimum detectable size, detection rate, coverage area, and latency. The search-based method modeled six different search patterns at varying starting positions. Results show that the minimum detectable size average for task-based was 47.6 cm in diameter while search-based methods ranged from 38.3 cm - 45.4 cm in diameter. Detection rate for task-based was 100% while the search-based ranged from 91.7% - 96.8%. Coverage area for task-based was 46% of the GEO belt and the search-based method ranged from 3.5% - 84.4%. Average latency (revisit time) for task-based was 78 minutes and search-based methods ranged from 62 - 469 minutes. It was found that task-based surveillance was the better method for current operational conditions by using a weighted decision criteria. However, as the number of RSOs increase there is a point at which the search method has better performance.

AFIT Designator

AFIT-ENV-MS-19-M-178

DTIC Accession Number

AD1077140

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