Cargando…

A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise

Due to the existence of marine environmental noise, coupled with the instability of underwater acoustic channel, ship-radiated noise (SRN) signals detected by sensors tend to suffer noise pollution as well as distortion caused by the transmission medium, making the denoising of the raw detected sign...

Descripción completa

Detalles Bibliográficos
Autores principales: Xie, Dongri, Sun, Haixin, Qi, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517155/
https://www.ncbi.nlm.nih.gov/pubmed/33286392
http://dx.doi.org/10.3390/e22060620
_version_ 1783587164761620480
author Xie, Dongri
Sun, Haixin
Qi, Jie
author_facet Xie, Dongri
Sun, Haixin
Qi, Jie
author_sort Xie, Dongri
collection PubMed
description Due to the existence of marine environmental noise, coupled with the instability of underwater acoustic channel, ship-radiated noise (SRN) signals detected by sensors tend to suffer noise pollution as well as distortion caused by the transmission medium, making the denoising of the raw detected signals the new focus in the field of underwater acoustic target recognition. In view of this, this paper presents a novel hybrid feature extraction scheme integrating improved variational mode decomposition (IVMD), normalized maximal information coefficient (norMIC) and permutation entropy (PE) for SRN signals. Firstly, the IVMD method is employed to decompose the SRN signals into a number of finite intrinsic mode functions (IMFs). The noise IMFs are then filtered out by a denoising method before PE extraction. Next, the MIC between each retained IMF and the raw SRN signal and PE of retained IMFs are calculated, respectively. After this, the norMICs are used to weigh the PE values of the retained IMFs and the sum of the weighted PE results is regarded as the classification parameter. Finally, the feature vectors are fed into the particle swarm optimization-based support vector machine multi-class classifier (PSO-SVM) to identify different types of SRN samples. The experimental results have indicated that the classification accuracy of the proposed method is as high as 99.1667%, which is much higher than that of other currently existing methods. Hence, the method proposed in this paper is more suitable for feature extraction of SRN signals in practical application.
format Online
Article
Text
id pubmed-7517155
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-75171552020-11-09 A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise Xie, Dongri Sun, Haixin Qi, Jie Entropy (Basel) Article Due to the existence of marine environmental noise, coupled with the instability of underwater acoustic channel, ship-radiated noise (SRN) signals detected by sensors tend to suffer noise pollution as well as distortion caused by the transmission medium, making the denoising of the raw detected signals the new focus in the field of underwater acoustic target recognition. In view of this, this paper presents a novel hybrid feature extraction scheme integrating improved variational mode decomposition (IVMD), normalized maximal information coefficient (norMIC) and permutation entropy (PE) for SRN signals. Firstly, the IVMD method is employed to decompose the SRN signals into a number of finite intrinsic mode functions (IMFs). The noise IMFs are then filtered out by a denoising method before PE extraction. Next, the MIC between each retained IMF and the raw SRN signal and PE of retained IMFs are calculated, respectively. After this, the norMICs are used to weigh the PE values of the retained IMFs and the sum of the weighted PE results is regarded as the classification parameter. Finally, the feature vectors are fed into the particle swarm optimization-based support vector machine multi-class classifier (PSO-SVM) to identify different types of SRN samples. The experimental results have indicated that the classification accuracy of the proposed method is as high as 99.1667%, which is much higher than that of other currently existing methods. Hence, the method proposed in this paper is more suitable for feature extraction of SRN signals in practical application. MDPI 2020-06-03 /pmc/articles/PMC7517155/ /pubmed/33286392 http://dx.doi.org/10.3390/e22060620 Text en © 2020 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
Xie, Dongri
Sun, Haixin
Qi, Jie
A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise
title A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise
title_full A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise
title_fullStr A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise
title_full_unstemmed A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise
title_short A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise
title_sort new feature extraction method based on improved variational mode decomposition, normalized maximal information coefficient and permutation entropy for ship-radiated noise
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517155/
https://www.ncbi.nlm.nih.gov/pubmed/33286392
http://dx.doi.org/10.3390/e22060620
work_keys_str_mv AT xiedongri anewfeatureextractionmethodbasedonimprovedvariationalmodedecompositionnormalizedmaximalinformationcoefficientandpermutationentropyforshipradiatednoise
AT sunhaixin anewfeatureextractionmethodbasedonimprovedvariationalmodedecompositionnormalizedmaximalinformationcoefficientandpermutationentropyforshipradiatednoise
AT qijie anewfeatureextractionmethodbasedonimprovedvariationalmodedecompositionnormalizedmaximalinformationcoefficientandpermutationentropyforshipradiatednoise
AT xiedongri newfeatureextractionmethodbasedonimprovedvariationalmodedecompositionnormalizedmaximalinformationcoefficientandpermutationentropyforshipradiatednoise
AT sunhaixin newfeatureextractionmethodbasedonimprovedvariationalmodedecompositionnormalizedmaximalinformationcoefficientandpermutationentropyforshipradiatednoise
AT qijie newfeatureextractionmethodbasedonimprovedvariationalmodedecompositionnormalizedmaximalinformationcoefficientandpermutationentropyforshipradiatednoise