Iterative Algorithm for High Resolution Frequency Estimation

Detalhes bibliográficos
Autor(a) principal: Duarte, Isabel M. P.
Data de Publicação: 2014
Outros Autores: Vieira, José M. N., Ferreira, Paulo J S G, Albuquerque, Daniel
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.19/2602
Resumo: Compressed sensing (CS) is a theory that allows us to recover sparse or compressible signals from a much smaller number of samples or measurements than with traditional methods. The problem of detection and estimation of the frequency of a signal is more difficult when the frequencies of the signal are not present on the DFT basis. The Fourier coefficients are not exactly sparse due to the leakage effect if the frequency is not a multiple of the fundamental frequency. In this work we present a high frequency resolution spectrum estimation algorithm that explores the CS, for this type of nonperiodic signal from finite number of samples. It takes advantage of the sparsity of the signal in the frequency domain. The algorithm transforms the DFT basis into a frame with a large number of vectors by inserting columns between some of the existing ones. The proposed algorithm can estimate the amplitudes and frequencies even when the frequencies are too close together, a particularly difficult situation which are not covered by most of the known algorithms. Simulation results show good convergence and a high resolution when compared with other algorithms
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spelling Iterative Algorithm for High Resolution Frequency EstimationCompressed sensingredundant framessignalsparse representationsspectral estimationCompressed sensing (CS) is a theory that allows us to recover sparse or compressible signals from a much smaller number of samples or measurements than with traditional methods. The problem of detection and estimation of the frequency of a signal is more difficult when the frequencies of the signal are not present on the DFT basis. The Fourier coefficients are not exactly sparse due to the leakage effect if the frequency is not a multiple of the fundamental frequency. In this work we present a high frequency resolution spectrum estimation algorithm that explores the CS, for this type of nonperiodic signal from finite number of samples. It takes advantage of the sparsity of the signal in the frequency domain. The algorithm transforms the DFT basis into a frame with a large number of vectors by inserting columns between some of the existing ones. The proposed algorithm can estimate the amplitudes and frequencies even when the frequencies are too close together, a particularly difficult situation which are not covered by most of the known algorithms. Simulation results show good convergence and a high resolution when compared with other algorithmsCenter for Studies in Education,Technologies and Health (CI&DETS)Prof. Chandratilak De Silva LiyanageRepositório Científico do Instituto Politécnico de ViseuDuarte, Isabel M. P.Vieira, José M. N.Ferreira, Paulo J S GAlbuquerque, Daniel2015-02-09T16:38:12Z2014-11-062014-11-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.19/2602por2010-371910.7763/IJIEEmetadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-01-16T15:25:57Zoai:repositorio.ipv.pt:10400.19/2602Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:41:44.861539Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Iterative Algorithm for High Resolution Frequency Estimation
title Iterative Algorithm for High Resolution Frequency Estimation
spellingShingle Iterative Algorithm for High Resolution Frequency Estimation
Duarte, Isabel M. P.
Compressed sensing
redundant frames
signal
sparse representations
spectral estimation
title_short Iterative Algorithm for High Resolution Frequency Estimation
title_full Iterative Algorithm for High Resolution Frequency Estimation
title_fullStr Iterative Algorithm for High Resolution Frequency Estimation
title_full_unstemmed Iterative Algorithm for High Resolution Frequency Estimation
title_sort Iterative Algorithm for High Resolution Frequency Estimation
author Duarte, Isabel M. P.
author_facet Duarte, Isabel M. P.
Vieira, José M. N.
Ferreira, Paulo J S G
Albuquerque, Daniel
author_role author
author2 Vieira, José M. N.
Ferreira, Paulo J S G
Albuquerque, Daniel
author2_role author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico de Viseu
dc.contributor.author.fl_str_mv Duarte, Isabel M. P.
Vieira, José M. N.
Ferreira, Paulo J S G
Albuquerque, Daniel
dc.subject.por.fl_str_mv Compressed sensing
redundant frames
signal
sparse representations
spectral estimation
topic Compressed sensing
redundant frames
signal
sparse representations
spectral estimation
description Compressed sensing (CS) is a theory that allows us to recover sparse or compressible signals from a much smaller number of samples or measurements than with traditional methods. The problem of detection and estimation of the frequency of a signal is more difficult when the frequencies of the signal are not present on the DFT basis. The Fourier coefficients are not exactly sparse due to the leakage effect if the frequency is not a multiple of the fundamental frequency. In this work we present a high frequency resolution spectrum estimation algorithm that explores the CS, for this type of nonperiodic signal from finite number of samples. It takes advantage of the sparsity of the signal in the frequency domain. The algorithm transforms the DFT basis into a frame with a large number of vectors by inserting columns between some of the existing ones. The proposed algorithm can estimate the amplitudes and frequencies even when the frequencies are too close together, a particularly difficult situation which are not covered by most of the known algorithms. Simulation results show good convergence and a high resolution when compared with other algorithms
publishDate 2014
dc.date.none.fl_str_mv 2014-11-06
2014-11-06T00:00:00Z
2015-02-09T16:38:12Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.19/2602
url http://hdl.handle.net/10400.19/2602
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv 2010-3719
10.7763/IJIEE
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dc.publisher.none.fl_str_mv Prof. Chandratilak De Silva Liyanage
publisher.none.fl_str_mv Prof. Chandratilak De Silva Liyanage
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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