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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8271499 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT berardengomarta seaspectralestimationusingarmamodels AT rossigiovannibattista seaspectralestimationusingarmamodels AT crennafrancesco seaspectralestimationusingarmamodels |