AESA adaptive beamforming using Deep Learning.

Authors: Bianco Simone, Feo Maurizio, Napoletano Paolo, Petraglia Giovanni, Raimondi Alberto, Vinetti Pietro. Published in: 2020 IEEE Radar Conference (RadarConf20)

In this research paper, we explored the possibility of implementing state-of-the-art deep learning techniques to perform fast one-pass beamforming of electromagnetic signals to speed up interference counter-measures in adversarial scenarios. We encode the signal into a 2D image to allow the use of computationally efficient convolutional neural networks to solve the beamforming task in a single pass of the network, skipping the slower iterative optimization approaches.