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

Autor:	Feng Jin, Reydesel Alejandro Cuevas, Siyang Cao, Arindam Sengupta
Rok vydání:	2020
Předmět:	Synthetic aperture radar Radar tracker business.industry Computer science Doppler radar ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications Ranging 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Convolutional neural network law.invention law Radar imaging Extremely high frequency 0202 electrical engineering electronic engineering information engineering ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS Artificial intelligence Radar business computer 0105 earth and related environmental sciences
Zdroj:	2020 IEEE Radar Conference (RadarConf20).
DOI:	10.1109/radarconf2043947.2020.9266501
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6881898e5c7dfff5e5ea735e4d467f2d https://doi.org/10.1109/radarconf2043947.2020.9266501 Zobrazit plný text záznamu