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Different Metrics for Singular Value Optimization in Near-Field Antenna Characterization

We deal with the use of different metrics in the framework of the Singular Value Optimization (SVO) technique for near-field antenna characterization. SVO extracts the maximum amount of information on an electromagnetic field over a certain domain from field samples on an acquisition domain, with a...

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Detalles Bibliográficos
Autores principales: Capozzoli, Amedeo, Curcio, Claudio, Liseno, Angelo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003039/
https://www.ncbi.nlm.nih.gov/pubmed/33803513
http://dx.doi.org/10.3390/s21062122
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author Capozzoli, Amedeo
Curcio, Claudio
Liseno, Angelo
author_facet Capozzoli, Amedeo
Curcio, Claudio
Liseno, Angelo
author_sort Capozzoli, Amedeo
collection PubMed
description We deal with the use of different metrics in the framework of the Singular Value Optimization (SVO) technique for near-field antenna characterization. SVO extracts the maximum amount of information on an electromagnetic field over a certain domain from field samples on an acquisition domain, with a priori information on the source, e.g., support information. It determines the field sample positions by optimizing a functional featuring the singular value dynamics of the radiation operator and representing a measure of the information collected by the field samples. Here, we discuss in detail and compare the use, in the framework of SVO, of different objective functionals and so of different information measures: Shannon number, mutual information, and Fisher information. The numerical results show that they yield a similar performance.
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spelling pubmed-80030392021-03-28 Different Metrics for Singular Value Optimization in Near-Field Antenna Characterization Capozzoli, Amedeo Curcio, Claudio Liseno, Angelo Sensors (Basel) Article We deal with the use of different metrics in the framework of the Singular Value Optimization (SVO) technique for near-field antenna characterization. SVO extracts the maximum amount of information on an electromagnetic field over a certain domain from field samples on an acquisition domain, with a priori information on the source, e.g., support information. It determines the field sample positions by optimizing a functional featuring the singular value dynamics of the radiation operator and representing a measure of the information collected by the field samples. Here, we discuss in detail and compare the use, in the framework of SVO, of different objective functionals and so of different information measures: Shannon number, mutual information, and Fisher information. The numerical results show that they yield a similar performance. MDPI 2021-03-18 /pmc/articles/PMC8003039/ /pubmed/33803513 http://dx.doi.org/10.3390/s21062122 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Capozzoli, Amedeo
Curcio, Claudio
Liseno, Angelo
Different Metrics for Singular Value Optimization in Near-Field Antenna Characterization
title Different Metrics for Singular Value Optimization in Near-Field Antenna Characterization
title_full Different Metrics for Singular Value Optimization in Near-Field Antenna Characterization
title_fullStr Different Metrics for Singular Value Optimization in Near-Field Antenna Characterization
title_full_unstemmed Different Metrics for Singular Value Optimization in Near-Field Antenna Characterization
title_short Different Metrics for Singular Value Optimization in Near-Field Antenna Characterization
title_sort different metrics for singular value optimization in near-field antenna characterization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003039/
https://www.ncbi.nlm.nih.gov/pubmed/33803513
http://dx.doi.org/10.3390/s21062122
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