A Review of Recent Advancements Including Machine Learning on Synthetic Aperture Radar using Millimeter-Wave Radar

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Author
Sengupta, Arindam
Jin, Feng
Cuevas, Reydesel Alejandro
Cao, Siyang
Affiliation
University of Arizona, Department of Electrical and Computer Engineering
Issue Date
2020-09-21
Keywords
Autonomous Vehicles
Convolutional Neural Networks
Generative Adversarial Networks
Millimeter Wave
Synthetic Aperture Radar

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Publisher
IEEE
Citation
A. Sengupta, F. Jin, R. A. Cuevas and S. Cao, "A Review of Recent Advancements Including Machine Learning on Synthetic Aperture Radar using Millimeter-Wave Radar," 2020 IEEE Radar Conference (RadarConf20), Florence, Italy, 2020, pp. 1-6, doi: 10.1109/RadarConf2043947.2020.9266501.
Journal
IEEE National Radar Conference - Proceedings
Rights
© 2020 IEEE.
Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
Abstract
In this paper, we review recent and emerging Synthetic Aperture Radar (SAR) applications using mm-Wave radar, ranging from concealed item detection to autonomous systems. Furthermore, relevant machine learning (ML) concepts are introduced and the review of ML applications in high-resolution mmWave SAR image enhancement and generation are presented. The paper is concluded with challenges and expectations of mmWave SAR imaging with emphasis on autonomous vehicles. ©2020 IEEE.
ISSN
1097-5659
DOI
10.1109/radarconf2043947.2020.9266501
Version
Final accepted manuscript
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