Resolution Enhancement of Spatial Parametric Methods via Regularization

The spatial spectral estimation problem has applications in a variety of fields, including radar, telecommunications, and biomedical engineering. Among the different approaches for estimating the spatial spectral pattern, there are several parametric methods, as the well-known multiple signal classi...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Gustavo Martin-del-Campo-Becerra, Sergio Serafin-Garcia, Andreas Reigber, Susana Ortega-Cisneros, Matteo Nannini
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Information criteria maximum likelihood (ML) model order selection (MOS) regularization synthetic aperture radar (SAR) tomography (TomoSAR) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/6aec7b32628646d0b9de47a51947251d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6aec7b32628646d0b9de47a51947251d
record_format	dspace
spelling	oai:doaj.org-article:6aec7b32628646d0b9de47a51947251d2021-11-19T00:00:17ZResolution Enhancement of Spatial Parametric Methods via Regularization2151-153510.1109/JSTARS.2021.3120281https://doaj.org/article/6aec7b32628646d0b9de47a51947251d2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9573368/https://doaj.org/toc/2151-1535The spatial spectral estimation problem has applications in a variety of fields, including radar, telecommunications, and biomedical engineering. Among the different approaches for estimating the spatial spectral pattern, there are several parametric methods, as the well-known multiple signal classification (MUSIC). Parametric methods like MUSIC are reduced to the problem of selecting an integer-valued parameter [so-called model order (MO)], which describes the number of signals impinging on the sensors array. Commonly, the best MO corresponds to the actual number of targets, nonetheless, relatively large model orders also retrieve good-fitted responses when the data generating mechanism is more complex than the models used to fit it. Most commonly employed MO selection (MOS) tools are based on information theoretic criteria (e.g., Akaike information criterion, minimum description length and efficient detection criterion). Normally, the implementation of these tools involves the eigenvalues decomposition of the data covariance matrix. A major drawback of such parametric methods (together with certain MOS tool) is the drastic accuracy decrease in adverse scenarios, particularly, with low signal-to-noise ratio, since the separation of the signal and noise subspaces becomes more difficult to achieve. Consequently, with the aim of refining the responses attained by parametric techniques like MUSIC, this article suggests utilizing regularization as a postprocessing step. Furthermore, as an alternative, this article also explores the possibility of selecting a single relatively large MO (rather than using MOS tools) and enhancing via regularization, the solutions retrieved by the treated parametric methods. In order to demonstrate the capabilities of this novel strategy, synthetic aperture radar tomography is considered as application.Gustavo Martin-del-Campo-BecerraSergio Serafin-GarciaAndreas ReigberSusana Ortega-CisnerosMatteo NanniniIEEEarticleInformation criteriamaximum likelihood (ML)model order selection (MOS)regularizationsynthetic aperture radar (SAR) tomography (TomoSAR)Ocean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11335-11351 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Information criteria maximum likelihood (ML) model order selection (MOS) regularization synthetic aperture radar (SAR) tomography (TomoSAR) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	Information criteria maximum likelihood (ML) model order selection (MOS) regularization synthetic aperture radar (SAR) tomography (TomoSAR) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Gustavo Martin-del-Campo-Becerra Sergio Serafin-Garcia Andreas Reigber Susana Ortega-Cisneros Matteo Nannini Resolution Enhancement of Spatial Parametric Methods via Regularization
description	The spatial spectral estimation problem has applications in a variety of fields, including radar, telecommunications, and biomedical engineering. Among the different approaches for estimating the spatial spectral pattern, there are several parametric methods, as the well-known multiple signal classification (MUSIC). Parametric methods like MUSIC are reduced to the problem of selecting an integer-valued parameter [so-called model order (MO)], which describes the number of signals impinging on the sensors array. Commonly, the best MO corresponds to the actual number of targets, nonetheless, relatively large model orders also retrieve good-fitted responses when the data generating mechanism is more complex than the models used to fit it. Most commonly employed MO selection (MOS) tools are based on information theoretic criteria (e.g., Akaike information criterion, minimum description length and efficient detection criterion). Normally, the implementation of these tools involves the eigenvalues decomposition of the data covariance matrix. A major drawback of such parametric methods (together with certain MOS tool) is the drastic accuracy decrease in adverse scenarios, particularly, with low signal-to-noise ratio, since the separation of the signal and noise subspaces becomes more difficult to achieve. Consequently, with the aim of refining the responses attained by parametric techniques like MUSIC, this article suggests utilizing regularization as a postprocessing step. Furthermore, as an alternative, this article also explores the possibility of selecting a single relatively large MO (rather than using MOS tools) and enhancing via regularization, the solutions retrieved by the treated parametric methods. In order to demonstrate the capabilities of this novel strategy, synthetic aperture radar tomography is considered as application.
format	article
author	Gustavo Martin-del-Campo-Becerra Sergio Serafin-Garcia Andreas Reigber Susana Ortega-Cisneros Matteo Nannini
author_facet	Gustavo Martin-del-Campo-Becerra Sergio Serafin-Garcia Andreas Reigber Susana Ortega-Cisneros Matteo Nannini
author_sort	Gustavo Martin-del-Campo-Becerra
title	Resolution Enhancement of Spatial Parametric Methods via Regularization
title_short	Resolution Enhancement of Spatial Parametric Methods via Regularization
title_full	Resolution Enhancement of Spatial Parametric Methods via Regularization
title_fullStr	Resolution Enhancement of Spatial Parametric Methods via Regularization
title_full_unstemmed	Resolution Enhancement of Spatial Parametric Methods via Regularization
title_sort	resolution enhancement of spatial parametric methods via regularization
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/6aec7b32628646d0b9de47a51947251d
work_keys_str_mv	AT gustavomartindelcampobecerra resolutionenhancementofspatialparametricmethodsviaregularization AT sergioserafingarcia resolutionenhancementofspatialparametricmethodsviaregularization AT andreasreigber resolutionenhancementofspatialparametricmethodsviaregularization AT susanaortegacisneros resolutionenhancementofspatialparametricmethodsviaregularization AT matteonannini resolutionenhancementofspatialparametricmethodsviaregularization
_version_	1718420673102086144

Resolution Enhancement of Spatial Parametric Methods via Regularization

Ejemplares similares