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Found 12 papers, 0 papers with code
On the Value of Online Learning for Radar Waveform Selection
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.
Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar
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.
When is Cognitive Radar Beneficial?
When should an online reinforcement learning-based frequency agile cognitive radar be expected to outperform a rule-based adaptive waveform selection strategy?
Timely Target Tracking in Cognitive Radar Networks
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.
Online Bayesian Meta-Learning for Cognitive Tracking Radar
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.
Linear Jamming Bandits: Sample-Efficient Learning for Non-Coherent Digital Jamming
As a result, convergence to the optimal jamming strategy can be slow, especially when the victim and jammer's symbols are not perfectly synchronized.
Universal Learning Waveform Selection Strategies for Adaptive Target Tracking
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.
Waveform Selection for Radar Tracking in Target Channels With Memory via Universal Learning
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.
Constrained Contextual Bandit Learning for Adaptive Radar Waveform Selection
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.
Efficient Online Learning for Cognitive Radar-Cellular Coexistence via Contextual Thompson Sampling
Additionally, we show that the TS learning scheme results in a favorable SINR distribution compared to other online learning algorithms.
Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments
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.
Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar
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.
