A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data

Autores
Zilberstein, Nicolás; Maya, Juan Augusto; Altieri, Andrés Oscar
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
An approach based on the Green function and the Born approximation is used for impulsive radio ultra-wideband (UWB) microwave imaging, in which a permittivity map of the illuminated scenario is estimated using the scattered fields measured at several positions. Two algorithms are applied to this model and compared: the first one solves the inversion problem using a linear operator. The second one is based on the Bayesian compressive sensing (BCS) technique, where the sparseness of the contrast function is introduced as extit{a priori} knowledge in order to improve the inverse mapping. In order to compare both methods, measurements in real scenarios are taken using an UWB radar prototype. The results with real measurements illustrate that, for the considered scenarios, the BCS imaging algorithm has a better performance in terms of range and cross-range resolution allowing object detection and shape reconstruction, with a reduced computational burden, and fewer space and frequency measurements, as compared to the linear operator.
Fil: Zilberstein, Nicolás. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Maya, Juan Augusto. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina
Fil: Altieri, Andrés Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; Argentina
Materia
MICROWAVE IMAGING
UTRA WIDEBAND RADAR
BAYESSIAN COMPRESSIVE SENSING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/156194
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental dataZilberstein, NicolásMaya, Juan AugustoAltieri, Andrés OscarMICROWAVE IMAGINGUTRA WIDEBAND RADARBAYESSIAN COMPRESSIVE SENSINGhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2An approach based on the Green function and the Born approximation is used for impulsive radio ultra-wideband (UWB) microwave imaging, in which a permittivity map of the illuminated scenario is estimated using the scattered fields measured at several positions. Two algorithms are applied to this model and compared: the first one solves the inversion problem using a linear operator. The second one is based on the Bayesian compressive sensing (BCS) technique, where the sparseness of the contrast function is introduced as extit{a priori} knowledge in order to improve the inverse mapping. In order to compare both methods, measurements in real scenarios are taken using an UWB radar prototype. The results with real measurements illustrate that, for the considered scenarios, the BCS imaging algorithm has a better performance in terms of range and cross-range resolution allowing object detection and shape reconstruction, with a reduced computational burden, and fewer space and frequency measurements, as compared to the linear operator.Fil: Zilberstein, Nicolás. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Maya, Juan Augusto. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; ArgentinaFil: Altieri, Andrés Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; ArgentinaInstitution of Engineering and Technology2021-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/156194Zilberstein, Nicolás; Maya, Juan Augusto; Altieri, Andrés Oscar; A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data; Institution of Engineering and Technology; Electronics Letters; 57; 2; 1-2021; 88-910013-5194CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/10.1049/ell2.12059info:eu-repo/semantics/altIdentifier/doi/10.1049/ell2.12059info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:00:17Zoai:ri.conicet.gov.ar:11336/156194instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:00:18.154CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data
title A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data
spellingShingle A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data
Zilberstein, Nicolás
MICROWAVE IMAGING
UTRA WIDEBAND RADAR
BAYESSIAN COMPRESSIVE SENSING
title_short A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data
title_full A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data
title_fullStr A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data
title_full_unstemmed A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data
title_sort A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data
dc.creator.none.fl_str_mv Zilberstein, Nicolás
Maya, Juan Augusto
Altieri, Andrés Oscar
author Zilberstein, Nicolás
author_facet Zilberstein, Nicolás
Maya, Juan Augusto
Altieri, Andrés Oscar
author_role author
author2 Maya, Juan Augusto
Altieri, Andrés Oscar
author2_role author
author
dc.subject.none.fl_str_mv MICROWAVE IMAGING
UTRA WIDEBAND RADAR
BAYESSIAN COMPRESSIVE SENSING
topic MICROWAVE IMAGING
UTRA WIDEBAND RADAR
BAYESSIAN COMPRESSIVE SENSING
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv An approach based on the Green function and the Born approximation is used for impulsive radio ultra-wideband (UWB) microwave imaging, in which a permittivity map of the illuminated scenario is estimated using the scattered fields measured at several positions. Two algorithms are applied to this model and compared: the first one solves the inversion problem using a linear operator. The second one is based on the Bayesian compressive sensing (BCS) technique, where the sparseness of the contrast function is introduced as extit{a priori} knowledge in order to improve the inverse mapping. In order to compare both methods, measurements in real scenarios are taken using an UWB radar prototype. The results with real measurements illustrate that, for the considered scenarios, the BCS imaging algorithm has a better performance in terms of range and cross-range resolution allowing object detection and shape reconstruction, with a reduced computational burden, and fewer space and frequency measurements, as compared to the linear operator.
Fil: Zilberstein, Nicolás. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Maya, Juan Augusto. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina
Fil: Altieri, Andrés Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; Argentina
description An approach based on the Green function and the Born approximation is used for impulsive radio ultra-wideband (UWB) microwave imaging, in which a permittivity map of the illuminated scenario is estimated using the scattered fields measured at several positions. Two algorithms are applied to this model and compared: the first one solves the inversion problem using a linear operator. The second one is based on the Bayesian compressive sensing (BCS) technique, where the sparseness of the contrast function is introduced as extit{a priori} knowledge in order to improve the inverse mapping. In order to compare both methods, measurements in real scenarios are taken using an UWB radar prototype. The results with real measurements illustrate that, for the considered scenarios, the BCS imaging algorithm has a better performance in terms of range and cross-range resolution allowing object detection and shape reconstruction, with a reduced computational burden, and fewer space and frequency measurements, as compared to the linear operator.
publishDate 2021
dc.date.none.fl_str_mv 2021-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/156194
Zilberstein, Nicolás; Maya, Juan Augusto; Altieri, Andrés Oscar; A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data; Institution of Engineering and Technology; Electronics Letters; 57; 2; 1-2021; 88-91
0013-5194
CONICET Digital
CONICET
url http://hdl.handle.net/11336/156194
identifier_str_mv Zilberstein, Nicolás; Maya, Juan Augusto; Altieri, Andrés Oscar; A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data; Institution of Engineering and Technology; Electronics Letters; 57; 2; 1-2021; 88-91
0013-5194
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/10.1049/ell2.12059
info:eu-repo/semantics/altIdentifier/doi/10.1049/ell2.12059
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institution of Engineering and Technology
publisher.none.fl_str_mv Institution of Engineering and Technology
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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