A survey on the low-dimensional-model-based electromagnetic imaging

Autores
Li, Lianlin; Hurtado, Martin; Xu, Feng; Zhang, Bing Chen; Jin, Tian; Cui, Tie Jun; Stevanovic, Marija Nikolic; Nehorai, Arye
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The low-dimensional-model-based electromagnetic imaging is an emerging member of the big family of computational imaging, by which the low-dimensional models of underlying signals are incorporated into both data acquisition systems and reconstruction algorithms for electromagnetic imaging, in order to improve the imaging performance and break the bottleneck of existing electromagnetic imaging methodologies. Over the past decade, we have witnessed profound impacts of the low-dimensional models on electromagnetic imaging. However, the low-dimensional-model-based electromagnetic imaging remains at its early stage, and many important issues relevant to practical applications need to be carefully investigated. Especially, we are in the big-data era of booming electromagnetic sensing, by which massive data are being collected for retrieving very detailed information of probed objects. This survey gives a comprehensive overview on the low-dimensional models of structure signals, along with its relevant theories and low-complexity algorithms of signal recovery. Afterwards, we review the recent advancements of low-dimensional-model-based electromagnetic imaging in various applied areas. We hope this survey could bridge the gap between the model-based signal processing and the electromagnetic imaging, advance the development of low-dimensional-model-based electromagnetic imaging, and serve as a basic reference in the future research of the electromagnetic imaging across various frequency ranges.
Fil: Li, Lianlin. Peking University; China
Fil: Hurtado, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Xu, Feng. Fudan University; China
Fil: Zhang, Bing Chen. Chinese Academy of Sciences; República de China
Fil: Jin, Tian. National University of defense Technology; China
Fil: Cui, Tie Jun. Southeast University; Bangladesh
Fil: Stevanovic, Marija Nikolic. University of Belgrade; Serbia
Fil: Nehorai, Arye. University of Washington; Estados Unidos
Materia
Electromagnetic imaging
Compressive sensing
Inverse scattering
Radar imaging
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/93765
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A survey on the low-dimensional-model-based electromagnetic imagingLi, LianlinHurtado, MartinXu, FengZhang, Bing ChenJin, TianCui, Tie JunStevanovic, Marija NikolicNehorai, AryeElectromagnetic imagingCompressive sensingInverse scatteringRadar imaginghttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2The low-dimensional-model-based electromagnetic imaging is an emerging member of the big family of computational imaging, by which the low-dimensional models of underlying signals are incorporated into both data acquisition systems and reconstruction algorithms for electromagnetic imaging, in order to improve the imaging performance and break the bottleneck of existing electromagnetic imaging methodologies. Over the past decade, we have witnessed profound impacts of the low-dimensional models on electromagnetic imaging. However, the low-dimensional-model-based electromagnetic imaging remains at its early stage, and many important issues relevant to practical applications need to be carefully investigated. Especially, we are in the big-data era of booming electromagnetic sensing, by which massive data are being collected for retrieving very detailed information of probed objects. This survey gives a comprehensive overview on the low-dimensional models of structure signals, along with its relevant theories and low-complexity algorithms of signal recovery. Afterwards, we review the recent advancements of low-dimensional-model-based electromagnetic imaging in various applied areas. We hope this survey could bridge the gap between the model-based signal processing and the electromagnetic imaging, advance the development of low-dimensional-model-based electromagnetic imaging, and serve as a basic reference in the future research of the electromagnetic imaging across various frequency ranges.Fil: Li, Lianlin. Peking University; ChinaFil: Hurtado, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Xu, Feng. Fudan University; ChinaFil: Zhang, Bing Chen. Chinese Academy of Sciences; República de ChinaFil: Jin, Tian. National University of defense Technology; ChinaFil: Cui, Tie Jun. Southeast University; BangladeshFil: Stevanovic, Marija Nikolic. University of Belgrade; SerbiaFil: Nehorai, Arye. University of Washington; Estados UnidosNow Publishers2018-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/93765Li, Lianlin; Hurtado, Martin; Xu, Feng; Zhang, Bing Chen; Jin, Tian; et al.; A survey on the low-dimensional-model-based electromagnetic imaging; Now Publishers; Foundations and Trends in Signal Processing; 12; 2; 6-2018; 107-1991932-8354CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.nowpublishers.com/article/Details/SIG-103info:eu-repo/semantics/altIdentifier/doi/10.1561/2000000103info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:46:53Zoai:ri.conicet.gov.ar:11336/93765instacron: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 09:46:53.809CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A survey on the low-dimensional-model-based electromagnetic imaging
title A survey on the low-dimensional-model-based electromagnetic imaging
spellingShingle A survey on the low-dimensional-model-based electromagnetic imaging
Li, Lianlin
Electromagnetic imaging
Compressive sensing
Inverse scattering
Radar imaging
title_short A survey on the low-dimensional-model-based electromagnetic imaging
title_full A survey on the low-dimensional-model-based electromagnetic imaging
title_fullStr A survey on the low-dimensional-model-based electromagnetic imaging
title_full_unstemmed A survey on the low-dimensional-model-based electromagnetic imaging
title_sort A survey on the low-dimensional-model-based electromagnetic imaging
dc.creator.none.fl_str_mv Li, Lianlin
Hurtado, Martin
Xu, Feng
Zhang, Bing Chen
Jin, Tian
Cui, Tie Jun
Stevanovic, Marija Nikolic
Nehorai, Arye
author Li, Lianlin
author_facet Li, Lianlin
Hurtado, Martin
Xu, Feng
Zhang, Bing Chen
Jin, Tian
Cui, Tie Jun
Stevanovic, Marija Nikolic
Nehorai, Arye
author_role author
author2 Hurtado, Martin
Xu, Feng
Zhang, Bing Chen
Jin, Tian
Cui, Tie Jun
Stevanovic, Marija Nikolic
Nehorai, Arye
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Electromagnetic imaging
Compressive sensing
Inverse scattering
Radar imaging
topic Electromagnetic imaging
Compressive sensing
Inverse scattering
Radar imaging
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The low-dimensional-model-based electromagnetic imaging is an emerging member of the big family of computational imaging, by which the low-dimensional models of underlying signals are incorporated into both data acquisition systems and reconstruction algorithms for electromagnetic imaging, in order to improve the imaging performance and break the bottleneck of existing electromagnetic imaging methodologies. Over the past decade, we have witnessed profound impacts of the low-dimensional models on electromagnetic imaging. However, the low-dimensional-model-based electromagnetic imaging remains at its early stage, and many important issues relevant to practical applications need to be carefully investigated. Especially, we are in the big-data era of booming electromagnetic sensing, by which massive data are being collected for retrieving very detailed information of probed objects. This survey gives a comprehensive overview on the low-dimensional models of structure signals, along with its relevant theories and low-complexity algorithms of signal recovery. Afterwards, we review the recent advancements of low-dimensional-model-based electromagnetic imaging in various applied areas. We hope this survey could bridge the gap between the model-based signal processing and the electromagnetic imaging, advance the development of low-dimensional-model-based electromagnetic imaging, and serve as a basic reference in the future research of the electromagnetic imaging across various frequency ranges.
Fil: Li, Lianlin. Peking University; China
Fil: Hurtado, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Xu, Feng. Fudan University; China
Fil: Zhang, Bing Chen. Chinese Academy of Sciences; República de China
Fil: Jin, Tian. National University of defense Technology; China
Fil: Cui, Tie Jun. Southeast University; Bangladesh
Fil: Stevanovic, Marija Nikolic. University of Belgrade; Serbia
Fil: Nehorai, Arye. University of Washington; Estados Unidos
description The low-dimensional-model-based electromagnetic imaging is an emerging member of the big family of computational imaging, by which the low-dimensional models of underlying signals are incorporated into both data acquisition systems and reconstruction algorithms for electromagnetic imaging, in order to improve the imaging performance and break the bottleneck of existing electromagnetic imaging methodologies. Over the past decade, we have witnessed profound impacts of the low-dimensional models on electromagnetic imaging. However, the low-dimensional-model-based electromagnetic imaging remains at its early stage, and many important issues relevant to practical applications need to be carefully investigated. Especially, we are in the big-data era of booming electromagnetic sensing, by which massive data are being collected for retrieving very detailed information of probed objects. This survey gives a comprehensive overview on the low-dimensional models of structure signals, along with its relevant theories and low-complexity algorithms of signal recovery. Afterwards, we review the recent advancements of low-dimensional-model-based electromagnetic imaging in various applied areas. We hope this survey could bridge the gap between the model-based signal processing and the electromagnetic imaging, advance the development of low-dimensional-model-based electromagnetic imaging, and serve as a basic reference in the future research of the electromagnetic imaging across various frequency ranges.
publishDate 2018
dc.date.none.fl_str_mv 2018-06
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/11336/93765
Li, Lianlin; Hurtado, Martin; Xu, Feng; Zhang, Bing Chen; Jin, Tian; et al.; A survey on the low-dimensional-model-based electromagnetic imaging; Now Publishers; Foundations and Trends in Signal Processing; 12; 2; 6-2018; 107-199
1932-8354
CONICET Digital
CONICET
url http://hdl.handle.net/11336/93765
identifier_str_mv Li, Lianlin; Hurtado, Martin; Xu, Feng; Zhang, Bing Chen; Jin, Tian; et al.; A survey on the low-dimensional-model-based electromagnetic imaging; Now Publishers; Foundations and Trends in Signal Processing; 12; 2; 6-2018; 107-199
1932-8354
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://www.nowpublishers.com/article/Details/SIG-103
info:eu-repo/semantics/altIdentifier/doi/10.1561/2000000103
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Now Publishers
publisher.none.fl_str_mv Now Publishers
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 13.069144

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