Value of Machine Learning and Cognition on Target Tracking
| dc.contributor.author | Rodriguez, Sebastian Daniel | en |
| dc.contributor.committeechair | Buehrer, Richard M. | en |
| dc.contributor.committeechair | Jakubisin, Daniel | en |
| dc.contributor.committeemember | Lanzerotti, Mary Yvonne | en |
| dc.contributor.department | Electrical Engineering | en |
| dc.date.accessioned | 2022-06-09T08:01:07Z | en |
| dc.date.available | 2022-06-09T08:01:07Z | en |
| dc.date.issued | 2022-06-08 | en |
| dc.description.abstract | In 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. | en |
| dc.description.abstractgeneral | With a newfound dependence on wireless transmission, the demand for electromagnetic spectrum allocations has vastly increased. In recent years the Federal Communications Commission has auctioned some previously restricted access frequency bands to public and commercial applications. While this enables the growth of faster and more widespread civilian communications, military radar systems which had been the priority users of those bands are now at risk of interference from new users. Current radar systems typically occupy fixed bands and are not yet well adjusted to sharing their allocated spectrum with other users. Cognitive radar systems have been proposed to monitor airwaves for potential interferences and autonomously manage band allocation to avoid the interferers. In this thesis, we study a learning algorithm that enables a radar system to actively monitor and select its bandwidth to ensure proper target tracking. In particular, we are interested in the value this learning algorithm 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. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:34877 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/110505 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Machine Learning | en |
| dc.subject | Cognitive Radar | en |
| dc.subject | Target tracking | en |
| dc.title | Value of Machine Learning and Cognition on Target Tracking | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Electrical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |