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Full Description
This practical resource provides an overview of machine learning (ML) approaches as applied to electromagnetics and antenna array processing. Detailed coverage of the main trends in ML, including uniform and random array processing (beamforming and detection of angle of arrival), antenna optimization, wave propagation, remote sensing, radar, and other aspects of electromagnetic design are explored. An introduction to machine learning principles and the most common machine learning architectures and algorithms used today in electromagnetics and other applications is presented, including basic neural networks, gaussian processes, support vector machines, kernel methods, deep learning, convolutional neural networks, and generative adversarial networks. Applications in electromagnetics and antenna array processing that are solved using machine learning are discussed, including antennas, remote sensing, and target classification.
Contents
Part I: Introduction to Machine Learning: Kernel methods for array processing; Support Vector Machines; Gaussian Processes for signal processing; Neural Networks; Convolutional neural networks; Recursive neural networks for signals; Restricted Boltzmann Machines; Generative Adversarial Networks; Part II: Applications in Electromagnetics and Antenna Array Signal Processing: Antenna Array Signal Processing; Radar and Remote Sensing; Computational Electromagnetics; Reconfigurable Antennas and Cognitive Radio; Design and Optimization of Antennas and RF devices; Wave Propagation and Modelling; Electromagnetics for Medicine and Healthcare.
