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Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar by Thornton, Charles E.; Buehrer, R. Michael; Martone, Anthony F.; Sherbondy, Kelly D. is a scholarly article available to read on EtoBox.

What is Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar about?

In this work, we first describe a framework for the application of Reinforcement Learning (RL) control to a radar system that operates in a congested spectral setting. We then compare the utility of several RL algorithms through a discussion of experiments performed on Commercial off-the-shelf (COTS) hardware. Each RL technique is evaluated in terms of convergence, radar detection performance achieved in a congested spectral environment, and the ability to share 100MHz spectrum with an uncooperative communications system. We examine policy iteration, which solves an environment posed as a Markov Decision Process (MDP) by directly solving for a stochastic mapping between environmental states and radar waveforms, as well as Deep RL techniques, which utilize a form of Q-Learning to approximate a parameterized function that is used by the radar to select optimal actions. We show that RL techniques are beneficial over a Sense-and-Avoid (SAA) scheme and discuss the conditions under which each approach is most effective.

Author

Thornton, Charles E.; Buehrer, R. Michael; Martone, Anthony F.; Sherbondy, Kelly D.

Published

2020

Language

EN