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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publication2020 IEEE Radar Conference, RadarConf 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728189420
DOIs
StatePublished - Sep 21 2020
Event2020 IEEE Radar Conference, RadarConf 2020 - Florence, Italy
Duration: Sep 21 2020Sep 25 2020

Publication series

NameIEEE National Radar Conference - Proceedings
Volume2020-September
ISSN (Print)1097-5659

Conference

Conference2020 IEEE Radar Conference, RadarConf 2020
Country/TerritoryItaly
CityFlorence
Period9/21/209/25/20

Keywords

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

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'A Review of Recent Advancements including Machine Learning on Synthetic Aperture Radar using Millimeter-Wave Radar'. Together they form a unique fingerprint.

Cite this