Creation of a Cognitive Radar with Machine Learning: Simulation and Implementation

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.

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

2019

Author dc:creator

• Kozy, Mark Alexander

Chair dc:contributor.committeechair

• Buehrer, R. Michael

Committee members dc:contributor.committeemember

• Reed, Jeffrey H.
• Ruohoniemi, J. Michael

• Cognitive radar
• Machine learning
• reinforcement learning
• tracking radar
• software defined radio

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:20201

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/89948