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Title:High-resolution millimeter-wave imaging for humans
Author(s):Chen, Luting
Contributor(s):Al-Hassanieh, Haitham
Subject(s):Millimeter-Wave imaging
FMCW radars
Neural Network
Abstract:The use of networking signals has been extended beyond communication to sensing, localization, robotics and autonomous systems in recent years. Specifically, recent advances in 5G millimeter wave (mmWave) have explored the possibility of expanding the use of mmWave beyond device communications and simple range sensing to a full-fledged imaging under low visibility conditions (fog, smog, snow, etc.). This thesis explores the use of mmWave imaging for humans, which could be incorporated into autonomous driving technology for pedestrian imaging in low visibility conditions. Unfortunately, certain challenges have been identified in mmWave imaging for humans, including its low resolution, the presence of fake artifacts resulting from multipath reflections, and the vibration of the human body. This thesis presents a system that can enable high-resolution mmWave imaging for humans that tries to address the above challenges by leveraging recent advances in deep learning, known as generative adversarial networks (GANs). In this thesis, we propose a GAN architecture that is customized to mmWave imaging and build a system that can significantly enhance the quality of mmWave images for humans.
Issue Date:2019-05
Date Available in IDEALS:2019-06-13

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