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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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 |
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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 |
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