Abstract
dc:description.abstractIn recent years previously restricted radio-frequency spectrum has been opened to civilian and industrial access in the United States. Because of this, high priority users such as the military and government need to develop systems that can adapt to the surrounding spectral environment which will suddenly be filled with new users. This thesis considers an environment with one tracking radar, a single target, and a communications system that can passively interfere with the radar system. Three separate agents, Sense and Avoid, Machine Learning, and "Optimal", are tasked with the channel selection problem for radar communications coexistence. Each agent is evaluated based on their ability to detect and avoid the interferer while also tracking a target accurately. In particular, in this thesis, we are interested in the value that machine learning algorithms can provide over and above simple approaches. This value is assessed based on the conflicting requirements of avoiding interference yet using as much of the spectrum for tracking as possible.
Degree
thesis:*- Name thesis:degree_name
- Master of Science
- Level thesis:degree_level
- masters
- Discipline thesis:degree_discipline
- Electrical Engineering
- Department dc:contributor.department
- Electrical Engineering
- Grantor dc:publisher
- Virginia Tech
- Year dc:date.issued
- 2022
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Rodriguez, Sebastian Daniel
- Chairs dc:contributor.committeechair
-
- Buehrer, Richard M.
- Jakubisin, Daniel
- Committee member dc:contributor.committeemember
-
- Lanzerotti, Mary Yvonne
Subjects
dc:subject × 3Rights
dc:rights- Statement dc:rights
-
- In Copyright
- Licence dc:rights.uri
- Language dc:language.iso
- en
Identifiers
dc:identifier.*- Dc Identifier Other
- vt_gsexam:34877
- OAI identifier oai:identifier
- oai:vtechworks.lib.vt.edu:10919/110505