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

Arindam Sengupta; Feng Jin; Reydesel Alejandro Cuevas; Siyang Cao

doi:10.1109/RadarConf2043947.2020.9266501

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

Arindam Sengupta, Feng Jin, Reydesel Alejandro Cuevas, Siyang Cao

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

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.

Original language	English (US)
Title of host publication	2020 IEEE Radar Conference, RadarConf 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728189420
DOIs	https://doi.org/10.1109/RadarConf2043947.2020.9266501
State	Published - Sep 21 2020
Event	2020 IEEE Radar Conference, RadarConf 2020 - Florence, Italy Duration: Sep 21 2020 → Sep 25 2020

Publication series

Name	IEEE National Radar Conference - Proceedings
Volume	2020-September
ISSN (Print)	1097-5659

Conference

Conference	2020 IEEE Radar Conference, RadarConf 2020
Country/Territory	Italy
City	Florence
Period	9/21/20 → 9/25/20

Keywords

Autonomous Vehicles
Convolutional Neural Networks
Generative Adversarial Networks
Millimeter Wave
Synthetic Aperture Radar

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/RadarConf2043947.2020.9266501

Cite this

Sengupta, A., Jin, F., Cuevas, R. A., & Cao, S. (2020). A Review of Recent Advancements including Machine Learning on Synthetic Aperture Radar using Millimeter-Wave Radar. In 2020 IEEE Radar Conference, RadarConf 2020 Article 9266501 (IEEE National Radar Conference - Proceedings; Vol. 2020-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RadarConf2043947.2020.9266501

A Review of Recent Advancements including Machine Learning on Synthetic Aperture Radar using Millimeter-Wave Radar. / Sengupta, Arindam; Jin, Feng; Cuevas, Reydesel Alejandro et al.
2020 IEEE Radar Conference, RadarConf 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9266501 (IEEE National Radar Conference - Proceedings; Vol. 2020-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sengupta, A, Jin, F, Cuevas, RA & Cao, S 2020, A Review of Recent Advancements including Machine Learning on Synthetic Aperture Radar using Millimeter-Wave Radar. in 2020 IEEE Radar Conference, RadarConf 2020., 9266501, IEEE National Radar Conference - Proceedings, vol. 2020-September, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE Radar Conference, RadarConf 2020, Florence, Italy, 9/21/20. https://doi.org/10.1109/RadarConf2043947.2020.9266501

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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.",

keywords = "Autonomous Vehicles, Convolutional Neural Networks, Generative Adversarial Networks, Millimeter Wave, Synthetic Aperture Radar",

author = "Arindam Sengupta and Feng Jin and Cuevas, {Reydesel Alejandro} and Siyang Cao",

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AB - 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.

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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