Available at: https://digitalcommons.calpoly.edu/theses/3167
Date of Award
12-2025
Degree Name
MS in Aerospace Engineering
Department/Program
Aerospace Engineering
College
College of Engineering
Advisor
Kira Abercromby
Advisor Department
Aerospace Engineering
Advisor College
College of Engineering
Abstract
An ever-growing interest in utilizing space for scientific, commercial, and defense applications has led to an exponential rise in orbital debris recent years. With this sharp increase in space objects comes a greatly increased risk of potential collisions, necessitating an increase in Space Situational Awareness. In order to best mitigate the risk of future collisions in an increasingly congested space environment, it is crucial to be able to accurately determine the orbits of these new objects for trajectory tracking. This process is initial orbit determination (IOD), and is especially difficult when only observation angles are available for short observation times. This thesis analyzed the accuracy of multiple variations of an IOD method using a genetic algorithm, a stochastic optimization metaheuristic mimicking natural selection. Gauss’s method, a well-researched classical IOD, was used as a baseline for analysis. Particular consideration was given towards the number of observation points available as well as the consequences of assuming a circular orbit. It was found that an assumed-circular orbit genetic algorithm IOD was nearly 100% successful in predicting secondary observations for the majority of the examined test cases with eccentricities up to 0.13, with limitations occurring at extremely short-arc, coplanar data. The GA was found to produce solutions with far less variability than Gauss’s method when dealing with noisy observation data, with Gauss’s method failing to converge to solutions for much of the GEO data.