Abstract:Purpose
The purpose of this paper is to present a deep-learning-based beamforming method for phased array weather radars, especially whose antenna arrays are equipped with large number of elements, for fast and accurate detection of weather observations.
Design/methodology/approach
The beamforming weights are computed by a convolutional neural network (CNN), which is trained with input–output pairs obtained from the Wiener solution.
Findings
To validate the robustness of the CNN-based beamformer, it is com… Show more
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