Tensor Methods for Blind Spatial Signature Estimation
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2014 |
| Tipo de documento: | Dissertação |
| Idioma: | por |
| Título da fonte: | Biblioteca Digital de Teses e Dissertações da UFC |
| Texto Completo: | http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=11635 |
Resumo: | In this dissertation the problem of spatial signature and direction of arrival estimation in Linear 2L-Shape and Planar arrays is investigated Methods based on tensor decompositions are proposed to treat the problem of estimating blind spatial signatures disregarding the use of training sequences and knowledge of the covariance structure of the sources By assuming that the power of the sources varies between successive time blocks decompositions for tensors of third and fourth orders obtained from spatial and spatio-temporal covariance of the received data in the array are proposed from which iterative algorithms are formulated to estimate spatial signatures of the sources Then greater spatial diversity is achieved by using the Spatial Smoothing in the 2L-Shape and Planar arrays In that case the estimation of the direction of arrival of the sources can not be obtained directly from the formulated algorithms The factorization of the Khatri-Rao product is then incorporated into these algorithms making it possible extracting estimates for the azimuth and elevation angles from matrices obtained using this method A distinguishing feature of the proposed tensor methods is their efficiency to treat the cases where the covariance matrix of the sources is non-diagonal and unknown which generally happens when working with sample data covariances computed from a reduced number of snapshots |
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info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisTensor Methods for Blind Spatial Signature EstimationMÃtodos tensoriais para estimaÃÃo cega de assinaturas espaciais2014-03-14Andrà Lima FÃrrer de Almeida77024494387http://lattes.cnpq.br/1183830514857314Charles Casimiro Cavalcante54039410378http://lattes.cnpq.br/4751699166195344JoÃo Paulo Carvalho Lustosa da Costa63967308391http://lattes.cnpq.br/1786889674911887GÃrard Favier04157888383http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4416935U6Paulo Ricardo Barboza GomesUniversidade Federal do CearÃPrograma de PÃs-GraduaÃÃo em Engenharia de TeleinformÃticaUFCBR DecomposiÃÃes tensoriaisArray signal processing spatial signature estimation tensor decompositionsENGENHARIA ELETRICAIn this dissertation the problem of spatial signature and direction of arrival estimation in Linear 2L-Shape and Planar arrays is investigated Methods based on tensor decompositions are proposed to treat the problem of estimating blind spatial signatures disregarding the use of training sequences and knowledge of the covariance structure of the sources By assuming that the power of the sources varies between successive time blocks decompositions for tensors of third and fourth orders obtained from spatial and spatio-temporal covariance of the received data in the array are proposed from which iterative algorithms are formulated to estimate spatial signatures of the sources Then greater spatial diversity is achieved by using the Spatial Smoothing in the 2L-Shape and Planar arrays In that case the estimation of the direction of arrival of the sources can not be obtained directly from the formulated algorithms The factorization of the Khatri-Rao product is then incorporated into these algorithms making it possible extracting estimates for the azimuth and elevation angles from matrices obtained using this method A distinguishing feature of the proposed tensor methods is their efficiency to treat the cases where the covariance matrix of the sources is non-diagonal and unknown which generally happens when working with sample data covariances computed from a reduced number of snapshotsNesta dissertaÃÃo o problema de estimaÃÃo de assinaturas espaciais e consequentemente da direÃÃo de chegada dos sinais incidentes em arranjos Linear 2L-Shape e Planar à investigado MÃtodos baseados em decomposiÃÃes tensoriais sÃo propostos para tratar o problema de estimaÃÃo cega de assinaturas espaciais desconsiderando a utilizaÃÃo de sequÃncias de treinamento e o conhecimento da estrutura de covariÃncia das fontes Ao assumir que a potÃncia das fontes varia entre blocos de tempos sucessivos decomposiÃÃes para tensores de terceira e quarta ordem obtidas a partir da covariÃncia espacial e espaÃo-temporal dos dados recebidos no arranjo de sensores sÃo propostas a partir das quais algoritmos iterativos sÃo formulados para estimar a assinatura espacial das fontes em seguida uma maior diversidade espacial à alcanÃada utilizando a tÃcnica Spatial Smoothing na recepÃÃo de sinais nos arranjos 2L-Shape e Planar Nesse caso as estimaÃÃes da direÃÃo de chegada das fontes nÃo podem ser obtidas diretamente a partir dos algoritmos formulados de forma que a fatoraÃÃo do produto de Khatri-Rao à incorporada a estes algoritmos tornando possÃvel a obtenÃÃo de estimaÃÃes para os Ãngulos de azimute e elevaÃÃo a partir das matrizes obtidas utilizando este mÃtodo Uma caracterÃstica marcante dos mÃtodos tensoriais propostos està presente na eficiÃncia obtida no tratamento de casos em que a matriz de covariÃncia das fontes à nÃo-diagonal e desconhecida o que geralmente ocorre quando se trabalha com covariÃncias de amostras reais calculadas a partir de um nÃmero reduzido de snapshotsFundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgicohttp://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=11635application/pdfinfo:eu-repo/semantics/openAccessporreponame:Biblioteca Digital de Teses e Dissertações da UFCinstname:Universidade Federal do Cearáinstacron:UFC2019-01-21T11:24:48Zmail@mail.com - |
| dc.title.en.fl_str_mv |
Tensor Methods for Blind Spatial Signature Estimation |
| dc.title.alternative.pt.fl_str_mv |
MÃtodos tensoriais para estimaÃÃo cega de assinaturas espaciais |
| title |
Tensor Methods for Blind Spatial Signature Estimation |
| spellingShingle |
Tensor Methods for Blind Spatial Signature Estimation Paulo Ricardo Barboza Gomes DecomposiÃÃes tensoriais Array signal processing spatial signature estimation tensor decompositions ENGENHARIA ELETRICA |
| title_short |
Tensor Methods for Blind Spatial Signature Estimation |
| title_full |
Tensor Methods for Blind Spatial Signature Estimation |
| title_fullStr |
Tensor Methods for Blind Spatial Signature Estimation |
| title_full_unstemmed |
Tensor Methods for Blind Spatial Signature Estimation |
| title_sort |
Tensor Methods for Blind Spatial Signature Estimation |
| author |
Paulo Ricardo Barboza Gomes |
| author_facet |
Paulo Ricardo Barboza Gomes |
| author_role |
author |
| dc.contributor.advisor1.fl_str_mv |
Andrà Lima FÃrrer de Almeida |
| dc.contributor.advisor1ID.fl_str_mv |
77024494387 |
| dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/1183830514857314 |
| dc.contributor.referee1.fl_str_mv |
Charles Casimiro Cavalcante |
| dc.contributor.referee1ID.fl_str_mv |
54039410378 |
| dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/4751699166195344 |
| dc.contributor.referee2.fl_str_mv |
JoÃo Paulo Carvalho Lustosa da Costa |
| dc.contributor.referee2ID.fl_str_mv |
63967308391 |
| dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/1786889674911887 |
| dc.contributor.referee3.fl_str_mv |
GÃrard Favier |
| dc.contributor.authorID.fl_str_mv |
04157888383 |
| dc.contributor.authorLattes.fl_str_mv |
http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4416935U6 |
| dc.contributor.author.fl_str_mv |
Paulo Ricardo Barboza Gomes |
| contributor_str_mv |
Andrà Lima FÃrrer de Almeida Charles Casimiro Cavalcante JoÃo Paulo Carvalho Lustosa da Costa GÃrard Favier |
| dc.subject.por.fl_str_mv |
DecomposiÃÃes tensoriais |
| topic |
DecomposiÃÃes tensoriais Array signal processing spatial signature estimation tensor decompositions ENGENHARIA ELETRICA |
| dc.subject.eng.fl_str_mv |
Array signal processing spatial signature estimation tensor decompositions |
| dc.subject.cnpq.fl_str_mv |
ENGENHARIA ELETRICA |
| dc.description.sponsorship.fl_txt_mv |
FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico |
| dc.description.abstract.por.fl_txt_mv |
In this dissertation the problem of spatial signature and direction of arrival estimation in Linear 2L-Shape and Planar arrays is investigated Methods based on tensor decompositions are proposed to treat the problem of estimating blind spatial signatures disregarding the use of training sequences and knowledge of the covariance structure of the sources By assuming that the power of the sources varies between successive time blocks decompositions for tensors of third and fourth orders obtained from spatial and spatio-temporal covariance of the received data in the array are proposed from which iterative algorithms are formulated to estimate spatial signatures of the sources Then greater spatial diversity is achieved by using the Spatial Smoothing in the 2L-Shape and Planar arrays In that case the estimation of the direction of arrival of the sources can not be obtained directly from the formulated algorithms The factorization of the Khatri-Rao product is then incorporated into these algorithms making it possible extracting estimates for the azimuth and elevation angles from matrices obtained using this method A distinguishing feature of the proposed tensor methods is their efficiency to treat the cases where the covariance matrix of the sources is non-diagonal and unknown which generally happens when working with sample data covariances computed from a reduced number of snapshots Nesta dissertaÃÃo o problema de estimaÃÃo de assinaturas espaciais e consequentemente da direÃÃo de chegada dos sinais incidentes em arranjos Linear 2L-Shape e Planar à investigado MÃtodos baseados em decomposiÃÃes tensoriais sÃo propostos para tratar o problema de estimaÃÃo cega de assinaturas espaciais desconsiderando a utilizaÃÃo de sequÃncias de treinamento e o conhecimento da estrutura de covariÃncia das fontes Ao assumir que a potÃncia das fontes varia entre blocos de tempos sucessivos decomposiÃÃes para tensores de terceira e quarta ordem obtidas a partir da covariÃncia espacial e espaÃo-temporal dos dados recebidos no arranjo de sensores sÃo propostas a partir das quais algoritmos iterativos sÃo formulados para estimar a assinatura espacial das fontes em seguida uma maior diversidade espacial à alcanÃada utilizando a tÃcnica Spatial Smoothing na recepÃÃo de sinais nos arranjos 2L-Shape e Planar Nesse caso as estimaÃÃes da direÃÃo de chegada das fontes nÃo podem ser obtidas diretamente a partir dos algoritmos formulados de forma que a fatoraÃÃo do produto de Khatri-Rao à incorporada a estes algoritmos tornando possÃvel a obtenÃÃo de estimaÃÃes para os Ãngulos de azimute e elevaÃÃo a partir das matrizes obtidas utilizando este mÃtodo Uma caracterÃstica marcante dos mÃtodos tensoriais propostos està presente na eficiÃncia obtida no tratamento de casos em que a matriz de covariÃncia das fontes à nÃo-diagonal e desconhecida o que geralmente ocorre quando se trabalha com covariÃncias de amostras reais calculadas a partir de um nÃmero reduzido de snapshots |
| description |
In this dissertation the problem of spatial signature and direction of arrival estimation in Linear 2L-Shape and Planar arrays is investigated Methods based on tensor decompositions are proposed to treat the problem of estimating blind spatial signatures disregarding the use of training sequences and knowledge of the covariance structure of the sources By assuming that the power of the sources varies between successive time blocks decompositions for tensors of third and fourth orders obtained from spatial and spatio-temporal covariance of the received data in the array are proposed from which iterative algorithms are formulated to estimate spatial signatures of the sources Then greater spatial diversity is achieved by using the Spatial Smoothing in the 2L-Shape and Planar arrays In that case the estimation of the direction of arrival of the sources can not be obtained directly from the formulated algorithms The factorization of the Khatri-Rao product is then incorporated into these algorithms making it possible extracting estimates for the azimuth and elevation angles from matrices obtained using this method A distinguishing feature of the proposed tensor methods is their efficiency to treat the cases where the covariance matrix of the sources is non-diagonal and unknown which generally happens when working with sample data covariances computed from a reduced number of snapshots |
| publishDate |
2014 |
| dc.date.issued.fl_str_mv |
2014-03-14 |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| status_str |
publishedVersion |
| format |
masterThesis |
| dc.identifier.uri.fl_str_mv |
http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=11635 |
| url |
http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=11635 |
| dc.language.iso.fl_str_mv |
por |
| language |
por |
| dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Universidade Federal do Cearà |
| dc.publisher.program.fl_str_mv |
Programa de PÃs-GraduaÃÃo em Engenharia de TeleinformÃtica |
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UFC |
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BR |
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Universidade Federal do Cearà |
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reponame:Biblioteca Digital de Teses e Dissertações da UFC instname:Universidade Federal do Ceará instacron:UFC |
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Biblioteca Digital de Teses e Dissertações da UFC |
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Biblioteca Digital de Teses e Dissertações da UFC |
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Universidade Federal do Ceará |
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UFC |
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UFC |
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mail@mail.com |
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