University of Illinois Urbana-Champaign

High-resolution millimeter-wave imaging for humans

Chen, Luting

This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.

Permalink

https://hdl.handle.net/2142/103999

Description

Title
High-resolution millimeter-wave imaging for humans
Author(s)
Chen, Luting
Contributor(s)
Al-Hassanieh, Haitham
Issue Date
2019-05
Keyword(s)
  • Millimeter-Wave imaging
  • FMCW radars
  • GANs
  • 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.
Type of Resource
text
Language
en
Permalink
http://hdl.handle.net/2142/103999

Owning Collections

Senior Theses - Electrical and Computer Engineering PRIMARY
The best of ECE undergraduate research