Design of a Co-Orbital Threat Identification System

With the increase in space traffic, proliferation of inexpensive launch opportunities, and interest from many countries in utilizing the space domain, threats to existing space assets are likely to increase dramatically in the coming years. The development of a system that can identify potential threats and alert space operators is vital to maintaining asset resiliency and security. The focus of this thesis is the design and evaluation of such a system. The design is comprised of the development of a classification hierarchy and the selection of machine learning models that will enable the identification of anomalous object behavior. The hierarchy is based on previous examples applied to object classification while reconsidering the assumption that a satellite may perform only one mission. The selected machine learning models perform both supervised classification of actively maneuvering objects and unsupervised identification of anomalous behavior within large satellite constellations. The evaluation process considers the independent adjustment of model hyperparameters to achieve optimal model settings. The optimal models perform both classification functions and return moderate accuracy. The system is applied to several case studies examining edge cases and what factors constitute a threatening object and what factors do not. Suggestions for improvement of the system in the future are presented.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Aerospace Engineering

Department dc:contributor.department

Aerospace and Ocean Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2022

Author dc:creator

• Whited, Derick John

Chair dc:contributor.committeechair

• Black, Jonathan T.

Committee members dc:contributor.committeemember

• Doyle, Daniel Drayson
• Fowler, Michael Chrispatrick

• Space Situational Awareness
• Machine learning
• Orbital Debris
• Classification
• Resident Space Object

Statement dc:rights

• Creative Commons Attribution 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:34080

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/109343