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Found 11 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.
Mode Selection and Target Classification in Cognitive Radar Networks
The goal of the network is to learn over many target tracks both the characteristics of the targets as well as the optimal action choices for each type of target.
Open and Closed-Loop Weight Selection for Pattern Control of Paraboloidal Reflector Antennas with Reconfigurable Rim Scattering
Second, since in many cases these weights require gains other than one, we develop a technique for determining unit-modulus weights so as to allow for surfaces which merely modify the phase of the scattered field, while substantially reducing the gain at arbitrary angles.
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.
Hybrid Cognition for Target Tracking in Cognitive Radar Networks
We show that in interference-limited spectrum, where the signal-to-interference-plus-noise ratio varies by channel and over time for a target with fixed radar cross section, a cognitive radar network is able to use information from the central coordinator in order to reduce the amount of time necessary to learn the optimal channel selection.
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.
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.
Model Order Estimation in the Presence of multipath Interference using Residual Convolutional Neural Networks
Model order estimation (MOE) is often a pre-requisite for Direction of Arrival (DoA) estimation.
Adversarial Multi-Player Bandits for Cognitive Radar Networks
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.
Multi-Target Localization Using Polarization Sensitive Arrays
The first method is iterative and uses only a series of observed bearings to multiple targets to establish clusters.
Direction of Arrival Estimation for a Vector Sensor Using Deep Neural Networks
A vector sensor, a type of sensor array with six collocated antennas to measure all electromagnetic field components of incident waves, has been shown to be advantageous in estimating the angle of arrival and polarization of the incident sources.
