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. / Master of Science / The increase in space traffic, proliferation of inexpensive launch opportunities, and interest from many countries in utilizing the space domain represent existential threats to existing spacecraft and operations in low-Earth orbit. Threats to the safe operation of spacecraft 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. This is accomplished by identifying a system architecture through evaluating current assumptions of what missions satellites are capable of performing. Following the system-level design, modules are proposed that utilize machine learning to identify satellite behavior that is abnormal. These modules are tested and tuned with optimal parameters to deliver improved identification performance. 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.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/109343 |
| Date | 15 March 2022 |
| Creators | Whited, Derick John |
| Contributors | Aerospace and Ocean Engineering, Black, Jonathan T., Doyle, Daniel Drayson, Fowler, Michael Chrispatrick |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf, application/pdf |
| Rights | Creative Commons Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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