Astrometric and Photometric Data Fusion in Machine Learning-Based Characterization of Resident Space Objects
type: CreativeWork
description: In this dissertation, a topic fundamental to Space Situational Awareness (SSA) is considered: resident space object (RSO) physical and dynamical property estimation (characterization). This topic is explored by leveraging both simulated and observational data of RSOs in conjunction with physics- and machine learning-based (ML) models to illustrate novel characterization methods for SSA. This is accomplished in three main parts: simulated and real-world case studies of object-specific dynamical and physical property inversion through Bayes’ theorem; initial results from a novel sensor system designed to autonomously collect data en masse on near-geosynchronous RSOs; and a deep machine learning approach to orbital regime identification of cislunar RSOs from telescope observational data. These topics represent significant advancement in the field of SSA in three different areas associated with the RSO characterization problem, where each provide many opportunities for future research and further advancement of the field. The first work put forward is a new method of RSO property estimation using Bayes’ theorem through the process of Markov Chain Monte Carlo (MCMC) sampling. This process will yield the most likely distributions for a desired set of space-object properties conditioned by observational data of the target. The feasibility of this method, as well as the performance for inversion of various physical and dynamical properties of an RSO is explored through both real-world case studies and simulation. The results show this to be a powerful method of property estimation reliant on sufficient measurements and a robust forward model. The first case study applies the technique to the near-Earth object (NEO) 2020 SO, detected by planetary defense survey telescopes. This object, which turned out to be the Centaur upper stage of the Surveyor 2 mission launched in 1966, was temporarily recaptured by the Earth out of heliocentric orbit. After two close fly-bys of the Ear…
abstract: In this dissertation, a topic fundamental to Space Situational Awareness (SSA) is considered: resident space object (RSO) physical and dynamical property estimation (characterization). This topic is explored by leveraging both simulated and observational data of RSOs in conjunction with physics- and machine learning-based (ML) models to illustrate novel characterization methods for SSA. This is accomplished in three main parts: simulated and real-world case studies of object-specific dynamical and physical property inversion through Bayes’ theorem; initial results from a novel sensor system designed to autonomously collect data en masse on near-geosynchronous RSOs; and a deep machine learning approach to orbital regime identification of cislunar RSOs from telescope observational data. These topics represent significant advancement in the field of SSA in three different areas associated with the RSO characterization problem, where each provide many opportunities for future research and further advancement of the field. The first work put forward is a new method of RSO property estimation using Bayes’ theorem through the process of Markov Chain Monte Carlo (MCMC) sampling. This process will yield the most likely distributions for a desired set of space-object properties conditioned by observational data of the target. The feasibility of this method, as well as the performance for inversion of various physical and dynamical properties of an RSO is explored through both real-world case studies and simulation. The results show this to be a powerful method of property estimation reliant on sufficient measurements and a robust forward model. The first case study applies the technique to the near-Earth object (NEO) 2020 SO, detected by planetary defense survey telescopes. This object, which turned out to be the Centaur upper stage of the Surveyor 2 mission launched in 1966, was temporarily recaptured by the Earth out of heliocentric orbit. After two close fly-bys of the Ear…
author: Campbell, Tanner
language: en
license: http://rightsstatements.org/vocab/InC/1.0/
date published: 2023
date modified: 2026-07-24T00:56:58Z
provider: University of Arizona
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: The University of Arizona.

PropertyValue
property ID: Handle
value: http://hdl.handle.net/10150/670331