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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/13715
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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 Técnicas2025-09-03T09:50:41Zoai: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:34982025-09-03 09:50:42.118CONICET 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 |
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1842269048214126592 |
score |
13.13397 |