Adaptive MCA-Matched Filter Algorithms for Binary Detection

Autores
Messina, Francisco; Cernuschi-Frías, Bruno
Año de publicación
2013
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this work, we present a method for signal-to-noise ratio maximization using a linear filter based on minor component analysis of the noise covariance matrix. As we will see, the greatest benefits are obtained when both filter and signal design are treated as a single problem. This general problem is then related to the minimization of the probability of error of a digital communication. In particular, the classical binary detection problem is considered when nonstationary and (possibly) nonwhite additive Gaussian noise is present. Two algorithms are given to solve the problem at hand with cuadratic and linear computational complexity with respect to the dimension of the problem.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Minor Component Analysis
Matched Filter
Optimal Signal Design
Binary Detection
Adaptive Algorithms
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/93429
Acceder
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network_name_str SEDICI (UNLP)
spelling Adaptive MCA-Matched Filter Algorithms for Binary DetectionMessina, FranciscoCernuschi-Frías, BrunoCiencias InformáticasMinor Component AnalysisMatched FilterOptimal Signal DesignBinary DetectionAdaptive AlgorithmsIn this work, we present a method for signal-to-noise ratio maximization using a linear filter based on minor component analysis of the noise covariance matrix. As we will see, the greatest benefits are obtained when both filter and signal design are treated as a single problem. This general problem is then related to the minimization of the probability of error of a digital communication. In particular, the classical binary detection problem is considered when nonstationary and (possibly) nonwhite additive Gaussian noise is present. Two algorithms are given to solve the problem at hand with cuadratic and linear computational complexity with respect to the dimension of the problem.Sociedad Argentina de Informática e Investigación Operativa2013-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf163-174http://sedici.unlp.edu.ar/handle/10915/93429enginfo:eu-repo/semantics/altIdentifier/issn/1850-2806info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:19:22Zoai:sedici.unlp.edu.ar:10915/93429Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:19:23.153SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Adaptive MCA-Matched Filter Algorithms for Binary Detection
title Adaptive MCA-Matched Filter Algorithms for Binary Detection
spellingShingle Adaptive MCA-Matched Filter Algorithms for Binary Detection
Messina, Francisco
Ciencias Informáticas
Minor Component Analysis
Matched Filter
Optimal Signal Design
Binary Detection
Adaptive Algorithms
title_short Adaptive MCA-Matched Filter Algorithms for Binary Detection
title_full Adaptive MCA-Matched Filter Algorithms for Binary Detection
title_fullStr Adaptive MCA-Matched Filter Algorithms for Binary Detection
title_full_unstemmed Adaptive MCA-Matched Filter Algorithms for Binary Detection
title_sort Adaptive MCA-Matched Filter Algorithms for Binary Detection
dc.creator.none.fl_str_mv Messina, Francisco
Cernuschi-Frías, Bruno
author Messina, Francisco
author_facet Messina, Francisco
Cernuschi-Frías, Bruno
author_role author
author2 Cernuschi-Frías, Bruno
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Minor Component Analysis
Matched Filter
Optimal Signal Design
Binary Detection
Adaptive Algorithms
topic Ciencias Informáticas
Minor Component Analysis
Matched Filter
Optimal Signal Design
Binary Detection
Adaptive Algorithms
dc.description.none.fl_txt_mv In this work, we present a method for signal-to-noise ratio maximization using a linear filter based on minor component analysis of the noise covariance matrix. As we will see, the greatest benefits are obtained when both filter and signal design are treated as a single problem. This general problem is then related to the minimization of the probability of error of a digital communication. In particular, the classical binary detection problem is considered when nonstationary and (possibly) nonwhite additive Gaussian noise is present. Two algorithms are given to solve the problem at hand with cuadratic and linear computational complexity with respect to the dimension of the problem.
Sociedad Argentina de Informática e Investigación Operativa
description In this work, we present a method for signal-to-noise ratio maximization using a linear filter based on minor component analysis of the noise covariance matrix. As we will see, the greatest benefits are obtained when both filter and signal design are treated as a single problem. This general problem is then related to the minimization of the probability of error of a digital communication. In particular, the classical binary detection problem is considered when nonstationary and (possibly) nonwhite additive Gaussian noise is present. Two algorithms are given to solve the problem at hand with cuadratic and linear computational complexity with respect to the dimension of the problem.
publishDate 2013
dc.date.none.fl_str_mv 2013-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/93429
url http://sedici.unlp.edu.ar/handle/10915/93429
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1850-2806
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
163-174
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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