no code implementations • 30 Nov 2023 • William W. Howard, Samuel R. Shebert, Anthony F. Martone, R. Michael Buehrer
Cognitive Radar Networks, which were popularized by Simon Haykin in 2006, have been proposed to address limitations with legacy radar installations.
no code implementations • 24 Jul 2023 • William W. Howard, Anthony F. Martone, R. Michael Buehrer
We discuss centralized and distributed approaches to solving this problem, taking into account the quality of node observations, the maneuverability of each target, and a limit on the rate at which any node may provide updates to the FC.
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 • 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 • 3 Aug 2021 • Samuel R. Shebert, Anthony F. Martone, R. Michael Buehrer
The closed set classifier achieves an average accuracy of 94. 5% for known signals with SNR's greater than 0 dB, but by design, has a 0% accuracy detecting signals from unknown classes.
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.