Projection matrix optimization for sparse signals in structured noise

Autores
Pazos, Sebastian; Hurtado, Martin; Muravchik, Carlos H.; Nehorai, Arye
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We consider the problem of estimating a signal which has been corrupted with structured noise. When the signal of interest accepts a sparse representation, only a small number of measurements are required to retain all the information. The measurements are mapped to a lower dimensional space through a projection matrix. We propose a method to optimize the design of this matrix where the objective is not only to reduce the amount of data to be processed but also to reject the undesired signal components. As a result, we reduce the computation time and the error on the estimation of the unknown parameters of the sparse model, with respect to the uncompressed data. The proposed method has tunable parameters that can affect its performance. Optimal tuning would require a comprehensive study of parameter variations and options. To avoid this learning burden, we also introduce a variant of the algorithm that is free from tuning, without significant loss of performance. Using synthetic data, we analyze the performance of the proposed algorithms and their robustness against errors in the model parameters. Additionally, we illustrate the performance of the method through a radar application using real clutter data with a still target and with a synthetic moving target.
Fil: Pazos, Sebastian. Universidad Nacional de la Plata. Facultad de Ingenieria. Departamento de Electrotecnia. Laboratorio de Electronica Ind., Control E Instrumentac.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Hurtado, Martin. Universidad Nacional de la Plata. Facultad de Ingenieria. Departamento de Electrotecnia. Laboratorio de Electronica Ind., Control E Instrumentac.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Muravchik, Carlos H.. Universidad Nacional de la Plata. Facultad de Ingenieria. Departamento de Electrotecnia. Laboratorio de Electronica Ind., Control E Instrumentac.; Argentina. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina
Fil: Nehorai, Arye. Washington University in St. Louis; Estados Unidos
Materia
Projection Matrix Optimization
Sparse Models
Compressive Sensing
Radar
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:11336/13715
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/13715
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Projection matrix optimization for sparse signals in structured noisePazos, SebastianHurtado, MartinMuravchik, Carlos H.Nehorai, AryeProjection Matrix OptimizationSparse ModelsCompressive SensingRadarhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2We consider the problem of estimating a signal which has been corrupted with structured noise. When the signal of interest accepts a sparse representation, only a small number of measurements are required to retain all the information. The measurements are mapped to a lower dimensional space through a projection matrix. We propose a method to optimize the design of this matrix where the objective is not only to reduce the amount of data to be processed but also to reject the undesired signal components. As a result, we reduce the computation time and the error on the estimation of the unknown parameters of the sparse model, with respect to the uncompressed data. The proposed method has tunable parameters that can affect its performance. Optimal tuning would require a comprehensive study of parameter variations and options. To avoid this learning burden, we also introduce a variant of the algorithm that is free from tuning, without significant loss of performance. Using synthetic data, we analyze the performance of the proposed algorithms and their robustness against errors in the model parameters. Additionally, we illustrate the performance of the method through a radar application using real clutter data with a still target and with a synthetic moving target.Fil: Pazos, Sebastian. Universidad Nacional de la Plata. Facultad de Ingenieria. Departamento de Electrotecnia. Laboratorio de Electronica Ind., Control E Instrumentac.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Hurtado, Martin. Universidad Nacional de la Plata. Facultad de Ingenieria. Departamento de Electrotecnia. Laboratorio de Electronica Ind., Control E Instrumentac.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Muravchik, Carlos H.. Universidad Nacional de la Plata. Facultad de Ingenieria. Departamento de Electrotecnia. Laboratorio de Electronica Ind., Control E Instrumentac.; Argentina. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; ArgentinaFil: Nehorai, Arye. Washington University in St. Louis; Estados UnidosInstitute Of Electrical And Electronics Engineers2015-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/13715Pazos, Sebastian; Hurtado, Martin; Muravchik, Carlos H.; Nehorai, Arye; Projection matrix optimization for sparse signals in structured noise; Institute Of Electrical And Electronics Engineers; Ieee Transactions On Signal Processing; 63; 15; 5-2015; 3902-39131053-587Xenginfo:eu-repo/semantics/altIdentifier/doi/10.1109/TSP.2015.2434328info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/7109949/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:46:01Zoai:ri.conicet.gov.ar:11336/13715instacron: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 09:46:01.821CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Projection matrix optimization for sparse signals in structured noise
title Projection matrix optimization for sparse signals in structured noise
spellingShingle Projection matrix optimization for sparse signals in structured noise
Pazos, Sebastian
Projection Matrix Optimization
Sparse Models
Compressive Sensing
Radar
title_short Projection matrix optimization for sparse signals in structured noise
title_full Projection matrix optimization for sparse signals in structured noise
title_fullStr Projection matrix optimization for sparse signals in structured noise
title_full_unstemmed Projection matrix optimization for sparse signals in structured noise
title_sort Projection matrix optimization for sparse signals in structured noise
dc.creator.none.fl_str_mv Pazos, Sebastian
Hurtado, Martin
Muravchik, Carlos H.
Nehorai, Arye
author Pazos, Sebastian
author_facet Pazos, Sebastian
Hurtado, Martin
Muravchik, Carlos H.
Nehorai, Arye
author_role author
author2 Hurtado, Martin
Muravchik, Carlos H.
Nehorai, Arye
author2_role author
author
author
dc.subject.none.fl_str_mv Projection Matrix Optimization
Sparse Models
Compressive Sensing
Radar
topic Projection Matrix Optimization
Sparse Models
Compressive Sensing
Radar
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv We consider the problem of estimating a signal which has been corrupted with structured noise. When the signal of interest accepts a sparse representation, only a small number of measurements are required to retain all the information. The measurements are mapped to a lower dimensional space through a projection matrix. We propose a method to optimize the design of this matrix where the objective is not only to reduce the amount of data to be processed but also to reject the undesired signal components. As a result, we reduce the computation time and the error on the estimation of the unknown parameters of the sparse model, with respect to the uncompressed data. The proposed method has tunable parameters that can affect its performance. Optimal tuning would require a comprehensive study of parameter variations and options. To avoid this learning burden, we also introduce a variant of the algorithm that is free from tuning, without significant loss of performance. Using synthetic data, we analyze the performance of the proposed algorithms and their robustness against errors in the model parameters. Additionally, we illustrate the performance of the method through a radar application using real clutter data with a still target and with a synthetic moving target.
Fil: Pazos, Sebastian. Universidad Nacional de la Plata. Facultad de Ingenieria. Departamento de Electrotecnia. Laboratorio de Electronica Ind., Control E Instrumentac.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Hurtado, Martin. Universidad Nacional de la Plata. Facultad de Ingenieria. Departamento de Electrotecnia. Laboratorio de Electronica Ind., Control E Instrumentac.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Muravchik, Carlos H.. Universidad Nacional de la Plata. Facultad de Ingenieria. Departamento de Electrotecnia. Laboratorio de Electronica Ind., Control E Instrumentac.; Argentina. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina
Fil: Nehorai, Arye. Washington University in St. Louis; Estados Unidos
description We consider the problem of estimating a signal which has been corrupted with structured noise. When the signal of interest accepts a sparse representation, only a small number of measurements are required to retain all the information. The measurements are mapped to a lower dimensional space through a projection matrix. We propose a method to optimize the design of this matrix where the objective is not only to reduce the amount of data to be processed but also to reject the undesired signal components. As a result, we reduce the computation time and the error on the estimation of the unknown parameters of the sparse model, with respect to the uncompressed data. The proposed method has tunable parameters that can affect its performance. Optimal tuning would require a comprehensive study of parameter variations and options. To avoid this learning burden, we also introduce a variant of the algorithm that is free from tuning, without significant loss of performance. Using synthetic data, we analyze the performance of the proposed algorithms and their robustness against errors in the model parameters. Additionally, we illustrate the performance of the method through a radar application using real clutter data with a still target and with a synthetic moving target.
publishDate 2015
dc.date.none.fl_str_mv 2015-05
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/11336/13715
Pazos, Sebastian; Hurtado, Martin; Muravchik, Carlos H.; Nehorai, Arye; Projection matrix optimization for sparse signals in structured noise; Institute Of Electrical And Electronics Engineers; Ieee Transactions On Signal Processing; 63; 15; 5-2015; 3902-3913
1053-587X
url http://hdl.handle.net/11336/13715
identifier_str_mv Pazos, Sebastian; Hurtado, Martin; Muravchik, Carlos H.; Nehorai, Arye; Projection matrix optimization for sparse signals in structured noise; Institute Of Electrical And Electronics Engineers; Ieee Transactions On Signal Processing; 63; 15; 5-2015; 3902-3913
1053-587X
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1109/TSP.2015.2434328
info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/7109949/
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 Institute Of Electrical And Electronics Engineers
publisher.none.fl_str_mv Institute Of Electrical And Electronics Engineers
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
_version_ 1844613438426316800
score 13.069144

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