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Near-distance raw and reconstructed ground based SAR data

Presented data includes two datasets named RealSAR-RAW and RealSAR-IMG. The first one contains unprocessed (raw) radar data obtained using Ground Based Synthetic Aperture Radar (GBSAR), while the second one contains images reconstructed using Omega-K algorithm applied to raw data from the first set....

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Autores principales: Turčinović, Filip, Kačan, Marin, Bojanjac, Dario, Bosiljevac, Marko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562663/
https://www.ncbi.nlm.nih.gov/pubmed/37822887
http://dx.doi.org/10.1016/j.dib.2023.109620
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author Turčinović, Filip
Kačan, Marin
Bojanjac, Dario
Bosiljevac, Marko
author_facet Turčinović, Filip
Kačan, Marin
Bojanjac, Dario
Bosiljevac, Marko
author_sort Turčinović, Filip
collection PubMed
description Presented data includes two datasets named RealSAR-RAW and RealSAR-IMG. The first one contains unprocessed (raw) radar data obtained using Ground Based Synthetic Aperture Radar (GBSAR), while the second one contains images reconstructed using Omega-K algorithm applied to raw data from the first set. The GBSAR system moves the radar sensor along the track to virtually extend (synthesize) the antenna aperture and provides imaging data of the area in front of the system. The used sensor was a Frequency Modulated Continuous Wave (FMCW) radar with a central frequency of 24 GHz and a 700 MHz wide bandwidth which in our case covered the observed scene in 30 steps with 1 cm step size. The measured (recorded) scenes were made on combinations of three test objects (bottles) made of different material (aluminum, glass, and plastic) in different positions. The aim was to develop a small dataset of GBSAR data useful for classification applications focused on distinguishing different materials from sparse radar data.
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spelling pubmed-105626632023-10-11 Near-distance raw and reconstructed ground based SAR data Turčinović, Filip Kačan, Marin Bojanjac, Dario Bosiljevac, Marko Data Brief Data Article Presented data includes two datasets named RealSAR-RAW and RealSAR-IMG. The first one contains unprocessed (raw) radar data obtained using Ground Based Synthetic Aperture Radar (GBSAR), while the second one contains images reconstructed using Omega-K algorithm applied to raw data from the first set. The GBSAR system moves the radar sensor along the track to virtually extend (synthesize) the antenna aperture and provides imaging data of the area in front of the system. The used sensor was a Frequency Modulated Continuous Wave (FMCW) radar with a central frequency of 24 GHz and a 700 MHz wide bandwidth which in our case covered the observed scene in 30 steps with 1 cm step size. The measured (recorded) scenes were made on combinations of three test objects (bottles) made of different material (aluminum, glass, and plastic) in different positions. The aim was to develop a small dataset of GBSAR data useful for classification applications focused on distinguishing different materials from sparse radar data. Elsevier 2023-09-25 /pmc/articles/PMC10562663/ /pubmed/37822887 http://dx.doi.org/10.1016/j.dib.2023.109620 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Turčinović, Filip
Kačan, Marin
Bojanjac, Dario
Bosiljevac, Marko
Near-distance raw and reconstructed ground based SAR data
title Near-distance raw and reconstructed ground based SAR data
title_full Near-distance raw and reconstructed ground based SAR data
title_fullStr Near-distance raw and reconstructed ground based SAR data
title_full_unstemmed Near-distance raw and reconstructed ground based SAR data
title_short Near-distance raw and reconstructed ground based SAR data
title_sort near-distance raw and reconstructed ground based sar data
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562663/
https://www.ncbi.nlm.nih.gov/pubmed/37822887
http://dx.doi.org/10.1016/j.dib.2023.109620
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