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20202026
most citedDeep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments

141 citations · 156 across the 15 of their papers we have counts for

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5 papers · 1 filter

eess.SP2026

Velocity Information Geometry of Coherent Intra-CPI Waveform Agility

Charles E Thornton

Spectrum sharing forces radars to vary carrier frequency and bandwidth on a pulse-to-pulse basis within a coherent processing interval (CPI). While the resulting range-Doppler dist…

eess.SP2024

Linear Jamming Bandits: Learning to Jam 5G-based Coded Communications Systems

Zachary Schutz, Daniel J. Jakubisin, Charles E. Thornton +1

We study jamming of an OFDM-modulated signal which employs forward error correction coding. We extend this to leverage reinforcement learning with a contextual bandit to jam a 5G-b…

eess.SP2023

On the Value of Online Learning for Radar Waveform Selection

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…

eess.SP2022

Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar

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…

eess.SP2020★ 141 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…