no code implementations • 21 Apr 2023 • Charles E. Thornton, R. Michael Buehrer
This paper attempts to characterize the kinds of physical scenarios in which an online learning-based cognitive radar is expected to reliably outperform a fixed rule-based waveform selection strategy, as well as the converse.
no code implementations • 1 Dec 2022 • Charles E. Thornton, William W. Howard, R. Michael Buehrer
This paper describes important considerations and challenges associated with online reinforcement-learning based waveform selection for target identification in frequency modulated continuous wave (FMCW) automotive radar systems.
no code implementations • 1 Dec 2022 • Charles E. Thornton, R. Michael Buehrer
When should an online reinforcement learning-based frequency agile cognitive radar be expected to outperform a rule-based adaptive waveform selection strategy?
no code implementations • 21 Nov 2022 • William W. Howard, Charles E. Thornton, R. Michael Buehrer
When each radar node in the network only is able to obtain noisy state measurements for a subset of the targets, the fusion center may not receive updates on every target during each update period.
no code implementations • 7 Jul 2022 • Charles E. Thornton, R. Michael Buehrer, Anthony F. Martone
This results in sample-efficient learning across a class of finite state target channels by exploiting inherent similarity across tracking scenes, attributed to common physical elements such as target type or clutter statistics.
no code implementations • 5 Jul 2022 • Charles E. Thornton, R. Michael Buehrer
As a result, convergence to the optimal jamming strategy can be slow, especially when the victim and jammer's symbols are not perfectly synchronized.
no code implementations • 10 Feb 2022 • Charles E. Thornton, R. Michael Buehrer, Harpreet S. Dhillon, Anthony F. Martone
More recently, reinforcement learning has been proposed for waveform selection, in which the problem is framed as a Markov decision process (MDP), allowing for long-term planning.
no code implementations • 2 Aug 2021 • Charles E. Thornton, R. Michael Buehrer, Anthony F. Martone
The proposed approach is tested in a simulation study, and is shown to provide tracking performance improvements over two state-of-the-art waveform selection schemes.
no code implementations • 9 Mar 2021 • Charles E. Thornton, R. Michael Buehrer, Anthony F. Martone
It is shown that the waveform selection problem can be effectively addressed using a linear contextual bandit formulation in a manner that is both computationally feasible and sample efficient.
no code implementations • 24 Aug 2020 • Charles E. Thornton, R. Michael Buehrer, Anthony F. Martone
Additionally, we show that the TS learning scheme results in a favorable SINR distribution compared to other online learning algorithms.
no code implementations • 23 Jun 2020 • Charles E. Thornton, Mark A. Kozy, R. Michael Buehrer, Anthony F. Martone, Kelly D. Sherbondy
In this paper, dynamic non-cooperative coexistence between a cognitive pulsed radar and a nearby communications system is addressed by applying nonlinear value function approximation via deep reinforcement learning (Deep RL) to develop a policy for optimal radar performance.
no code implementations • 6 Jan 2020 • Charles E. Thornton, R. Michael Buehrer, Anthony F. Martone, Kelly D. Sherbondy
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.