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Found 10 papers, 0 papers with code
Mode Selection in Cognitive Radar Networks
Cognitive Radar Networks, which were popularized by Simon Haykin in 2006, have been proposed to address limitations with legacy radar installations.
Timely Target Tracking: Distributed Updating in Cognitive Radar Networks
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.
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.
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.
Open Set Wireless Standard Classification Using Convolutional Neural Networks
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.
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.
