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20172022
most citedDeep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments

141 citations · 148 across the 10 of their papers we have counts for

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5 papers · 1 filter

eess.SP2021

Adversarial Multi-Player Bandits for Cognitive Radar Networks

William W. Howard, R. M. Buehrer, Anthony Martone

We model a radar network as an adversarial bandit problem, where the environment pre-selects reward sequences for each of several actions available to the network. This excludes en…

eess.SP20214 cited

Classification of Common Waveforms Including a Watchdog for Unknown Signals

C. Tanner Fredieu, Justin Bui, Anthony Martone +3

In this paper, we examine the use of a deep multi-layer perceptron model architecture to classify received signal samples as coming from one of four common waveforms, Single Carrie…

eess.SP2021

Open Set Wireless Standard Classification Using Convolutional Neural Networks

Samuel R. Shebert, Anthony F. Martone, R. Michael Buehrer

In congested electromagnetic environments, cognitive radios require knowledge about other emitters in order to optimize their dynamic spectrum access strategy. Deep learning classi…

eess.SP2020141 cited

Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments

Charles E. Thornton, Mark A. Kozy, R. Michael Buehrer +2

In this paper, dynamic non-cooperative coexistence between a cognitive pulsed radar and a nearby communications system is addressed by applying nonlinear value function approximati…

eess.SP20173 cited

Coexistence between Communication and Radar Systems - A Survey

Mina Labib, Vuk Marojevic, Anthony F. Martone +2

Data traffic demand in cellular networks has been tremendously growing and has led to creating congested RF environment. Accordingly, innovative approaches for spectrum sharing hav…