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Sea Spectral Estimation Using ARMA Models

This paper deals with the spectral estimation of sea wave elevation time series by means of ARMA models. To start, the procedure to estimate the ARMA coefficients, based on the use of the Prony’s method applied to the auto-covariance series, is presented. Afterwards, an analysis on how the parameter...

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Detalles Bibliográficos
Autores principales: Berardengo, Marta, Rossi, Giovanni Battista, Crenna, Francesco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271499/
https://www.ncbi.nlm.nih.gov/pubmed/34201469
http://dx.doi.org/10.3390/s21134280
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author Berardengo, Marta
Rossi, Giovanni Battista
Crenna, Francesco
author_facet Berardengo, Marta
Rossi, Giovanni Battista
Crenna, Francesco
author_sort Berardengo, Marta
collection PubMed
description This paper deals with the spectral estimation of sea wave elevation time series by means of ARMA models. To start, the procedure to estimate the ARMA coefficients, based on the use of the Prony’s method applied to the auto-covariance series, is presented. Afterwards, an analysis on how the parameters involved in the ARMA reconstruction procedure—for example, the signal time length, the number of poles and data used—affect the spectral estimates is carried out, providing evidence on their effect on the accuracy of results. This allowed us to provide guidelines on how to set these parameters in order to make the ARMA model as accurate as possible. The paper focuses on mono-modal sea states. Nevertheless, examples also related to bi-modal sea states are discussed.
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spelling pubmed-82714992021-07-11 Sea Spectral Estimation Using ARMA Models Berardengo, Marta Rossi, Giovanni Battista Crenna, Francesco Sensors (Basel) Article This paper deals with the spectral estimation of sea wave elevation time series by means of ARMA models. To start, the procedure to estimate the ARMA coefficients, based on the use of the Prony’s method applied to the auto-covariance series, is presented. Afterwards, an analysis on how the parameters involved in the ARMA reconstruction procedure—for example, the signal time length, the number of poles and data used—affect the spectral estimates is carried out, providing evidence on their effect on the accuracy of results. This allowed us to provide guidelines on how to set these parameters in order to make the ARMA model as accurate as possible. The paper focuses on mono-modal sea states. Nevertheless, examples also related to bi-modal sea states are discussed. MDPI 2021-06-23 /pmc/articles/PMC8271499/ /pubmed/34201469 http://dx.doi.org/10.3390/s21134280 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Berardengo, Marta
Rossi, Giovanni Battista
Crenna, Francesco
Sea Spectral Estimation Using ARMA Models
title Sea Spectral Estimation Using ARMA Models
title_full Sea Spectral Estimation Using ARMA Models
title_fullStr Sea Spectral Estimation Using ARMA Models
title_full_unstemmed Sea Spectral Estimation Using ARMA Models
title_short Sea Spectral Estimation Using ARMA Models
title_sort sea spectral estimation using arma models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271499/
https://www.ncbi.nlm.nih.gov/pubmed/34201469
http://dx.doi.org/10.3390/s21134280
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