In this paper we address radar-communication coexistence by modelling the radar environment as a Markov Decision Process (MDP), and then apply Deep-Q Learning to optimize radar performance. The radar environment includes a single point target and a communications system that will potentially interfere with the radar. We demonstrate that the Deep-Q Network (DQN) we construct is able to successfully avoid interfering with the communication system to improve its performance. We also show that the DQN method outperforms previous methods in terms of memory and handling new situations. In this thesis we also address the application of the MDP into a software defined radio (SDR) USRP X310 by utilizing the software LabVIEW to communicate with and control the SDR. / Master of Science / In this thesis we develop methods for creating and implementing algorithms for a cognitive radar. A cognitive radar is a radar that is able to sense its environment and avoid any other communication system that may interfere with its operation. We discuss the predictive methods we used to sense and avoid the other communication systems as well as how we implemented this using a software defined radar based on the USRP X310.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/89948 |
| Date | 12 June 2019 |
| Creators | Kozy, Mark Alexander |
| Contributors | Electrical Engineering, Buehrer, R. Michael, Reed, Jeffrey H., Ruohoniemi, J. Michael |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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