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dc.contributor.author
Pazos, Sebastian
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dc.contributor.author
Hurtado, Martin
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dc.contributor.author
Muravchik, Carlos H.
dc.contributor.author
Nehorai, Arye
dc.date.available
2017-03-09T21:15:46Z
dc.date.issued
2015-05
dc.identifier.citation
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
dc.identifier.issn
1053-587X
dc.identifier.uri
http://hdl.handle.net/11336/13715
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Institute Of Electrical And Electronics Engineers
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Projection Matrix Optimization
dc.subject
Sparse Models
dc.subject
Compressive Sensing
dc.subject
Radar
dc.subject.classification
Telecomunicaciones
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dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
Projection matrix optimization for sparse signals in structured noise
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-03-08T15:40:41Z
dc.journal.volume
63
dc.journal.number
15
dc.journal.pagination
3902-3913
dc.journal.pais
Estados Unidos
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dc.journal.ciudad
Piscataway
dc.description.fil
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
dc.description.fil
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
dc.description.fil
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
dc.description.fil
Fil: Nehorai, Arye. Washington University in St. Louis; Estados Unidos
dc.journal.title
Ieee Transactions On Signal Processing
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/TSP.2015.2434328
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/7109949/
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