A comparative study of four types of multi-scale entropies in feature extraction of underwater acoustic signals for potential GNSS positioning applications

The combination of underwater acoustic processing and the Global Navigation Satellite System (GNSS) has achieved remarkable economic benefits in offshore operations. As the key technology of GNNS positioning, feature extraction of underwater acoustic signals is affected by the complex marine environ...
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Gespeichert in:

Autor*in:	Danning Zhao [verfasserIn] Yu Lei [verfasserIn] Jinsong Xu [verfasserIn] Hongbing Cai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	multi-scale fuzzy entropy multi-scale dispersion entropy multi-scale permutation entropy multi-scale sample entropy marine ambient noise ship-radiated noise

Übergeordnetes Werk:	In: Frontiers in Physics - Frontiers Media S.A., 2014, 10(2022)
Übergeordnetes Werk:	volume:10 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fphy.2022.1058474

Katalog-ID:	DOAJ086980467

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spelling	10.3389/fphy.2022.1058474 doi (DE-627)DOAJ086980467 (DE-599)DOAJ9cc9571135d545e38f2079d7f8e5e714 DE-627 ger DE-627 rakwb eng QC1-999 Danning Zhao verfasserin aut A comparative study of four types of multi-scale entropies in feature extraction of underwater acoustic signals for potential GNSS positioning applications 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The combination of underwater acoustic processing and the Global Navigation Satellite System (GNSS) has achieved remarkable economic benefits in offshore operations. As the key technology of GNNS positioning, feature extraction of underwater acoustic signals is affected by the complex marine environment. To extract more effective information from underwater acoustic signals, we use four types of multi-scale entropies, including multi-scale sample entropy (MSE), multi-scale fuzzy entropy (MFE), multi-scale permutation entropy (MPE), and multi-scale dispersion entropy (MDE), to analyze and distinguish underwater acoustic signals. In this study, two groups of real-word underwater acoustic signal experiments were performed for feature extraction of ship-radiated noises (SRNs) and ambient noises (ANs). The results indicated that the performance of the MFE-based feature extraction method is superior to that of feature extraction methods based on the other three entropies under the same number of features, and the highest average recognition rate (ARR) of the MFE-based feature extraction method for SRNs reaches 100% when the number of features is 3. multi-scale fuzzy entropy multi-scale dispersion entropy multi-scale permutation entropy multi-scale sample entropy marine ambient noise ship-radiated noise Physics Danning Zhao verfasserin aut Danning Zhao verfasserin aut Yu Lei verfasserin aut Jinsong Xu verfasserin aut Hongbing Cai verfasserin aut In Frontiers in Physics Frontiers Media S.A., 2014 10(2022) (DE-627)750371749 (DE-600)2721033-9 2296424X nnns volume:10 year:2022 https://doi.org/10.3389/fphy.2022.1058474 kostenfrei https://doaj.org/article/9cc9571135d545e38f2079d7f8e5e714 kostenfrei https://www.frontiersin.org/articles/10.3389/fphy.2022.1058474/full kostenfrei https://doaj.org/toc/2296-424X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022
allfields_unstemmed	10.3389/fphy.2022.1058474 doi (DE-627)DOAJ086980467 (DE-599)DOAJ9cc9571135d545e38f2079d7f8e5e714 DE-627 ger DE-627 rakwb eng QC1-999 Danning Zhao verfasserin aut A comparative study of four types of multi-scale entropies in feature extraction of underwater acoustic signals for potential GNSS positioning applications 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The combination of underwater acoustic processing and the Global Navigation Satellite System (GNSS) has achieved remarkable economic benefits in offshore operations. As the key technology of GNNS positioning, feature extraction of underwater acoustic signals is affected by the complex marine environment. To extract more effective information from underwater acoustic signals, we use four types of multi-scale entropies, including multi-scale sample entropy (MSE), multi-scale fuzzy entropy (MFE), multi-scale permutation entropy (MPE), and multi-scale dispersion entropy (MDE), to analyze and distinguish underwater acoustic signals. In this study, two groups of real-word underwater acoustic signal experiments were performed for feature extraction of ship-radiated noises (SRNs) and ambient noises (ANs). The results indicated that the performance of the MFE-based feature extraction method is superior to that of feature extraction methods based on the other three entropies under the same number of features, and the highest average recognition rate (ARR) of the MFE-based feature extraction method for SRNs reaches 100% when the number of features is 3. multi-scale fuzzy entropy multi-scale dispersion entropy multi-scale permutation entropy multi-scale sample entropy marine ambient noise ship-radiated noise Physics Danning Zhao verfasserin aut Danning Zhao verfasserin aut Yu Lei verfasserin aut Jinsong Xu verfasserin aut Hongbing Cai verfasserin aut In Frontiers in Physics Frontiers Media S.A., 2014 10(2022) (DE-627)750371749 (DE-600)2721033-9 2296424X nnns volume:10 year:2022 https://doi.org/10.3389/fphy.2022.1058474 kostenfrei https://doaj.org/article/9cc9571135d545e38f2079d7f8e5e714 kostenfrei https://www.frontiersin.org/articles/10.3389/fphy.2022.1058474/full kostenfrei https://doaj.org/toc/2296-424X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022
allfieldsGer	10.3389/fphy.2022.1058474 doi (DE-627)DOAJ086980467 (DE-599)DOAJ9cc9571135d545e38f2079d7f8e5e714 DE-627 ger DE-627 rakwb eng QC1-999 Danning Zhao verfasserin aut A comparative study of four types of multi-scale entropies in feature extraction of underwater acoustic signals for potential GNSS positioning applications 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The combination of underwater acoustic processing and the Global Navigation Satellite System (GNSS) has achieved remarkable economic benefits in offshore operations. As the key technology of GNNS positioning, feature extraction of underwater acoustic signals is affected by the complex marine environment. To extract more effective information from underwater acoustic signals, we use four types of multi-scale entropies, including multi-scale sample entropy (MSE), multi-scale fuzzy entropy (MFE), multi-scale permutation entropy (MPE), and multi-scale dispersion entropy (MDE), to analyze and distinguish underwater acoustic signals. In this study, two groups of real-word underwater acoustic signal experiments were performed for feature extraction of ship-radiated noises (SRNs) and ambient noises (ANs). The results indicated that the performance of the MFE-based feature extraction method is superior to that of feature extraction methods based on the other three entropies under the same number of features, and the highest average recognition rate (ARR) of the MFE-based feature extraction method for SRNs reaches 100% when the number of features is 3. multi-scale fuzzy entropy multi-scale dispersion entropy multi-scale permutation entropy multi-scale sample entropy marine ambient noise ship-radiated noise Physics Danning Zhao verfasserin aut Danning Zhao verfasserin aut Yu Lei verfasserin aut Jinsong Xu verfasserin aut Hongbing Cai verfasserin aut In Frontiers in Physics Frontiers Media S.A., 2014 10(2022) (DE-627)750371749 (DE-600)2721033-9 2296424X nnns volume:10 year:2022 https://doi.org/10.3389/fphy.2022.1058474 kostenfrei https://doaj.org/article/9cc9571135d545e38f2079d7f8e5e714 kostenfrei https://www.frontiersin.org/articles/10.3389/fphy.2022.1058474/full kostenfrei https://doaj.org/toc/2296-424X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022
allfieldsSound	10.3389/fphy.2022.1058474 doi (DE-627)DOAJ086980467 (DE-599)DOAJ9cc9571135d545e38f2079d7f8e5e714 DE-627 ger DE-627 rakwb eng QC1-999 Danning Zhao verfasserin aut A comparative study of four types of multi-scale entropies in feature extraction of underwater acoustic signals for potential GNSS positioning applications 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The combination of underwater acoustic processing and the Global Navigation Satellite System (GNSS) has achieved remarkable economic benefits in offshore operations. As the key technology of GNNS positioning, feature extraction of underwater acoustic signals is affected by the complex marine environment. To extract more effective information from underwater acoustic signals, we use four types of multi-scale entropies, including multi-scale sample entropy (MSE), multi-scale fuzzy entropy (MFE), multi-scale permutation entropy (MPE), and multi-scale dispersion entropy (MDE), to analyze and distinguish underwater acoustic signals. In this study, two groups of real-word underwater acoustic signal experiments were performed for feature extraction of ship-radiated noises (SRNs) and ambient noises (ANs). The results indicated that the performance of the MFE-based feature extraction method is superior to that of feature extraction methods based on the other three entropies under the same number of features, and the highest average recognition rate (ARR) of the MFE-based feature extraction method for SRNs reaches 100% when the number of features is 3. multi-scale fuzzy entropy multi-scale dispersion entropy multi-scale permutation entropy multi-scale sample entropy marine ambient noise ship-radiated noise Physics Danning Zhao verfasserin aut Danning Zhao verfasserin aut Yu Lei verfasserin aut Jinsong Xu verfasserin aut Hongbing Cai verfasserin aut In Frontiers in Physics Frontiers Media S.A., 2014 10(2022) (DE-627)750371749 (DE-600)2721033-9 2296424X nnns volume:10 year:2022 https://doi.org/10.3389/fphy.2022.1058474 kostenfrei https://doaj.org/article/9cc9571135d545e38f2079d7f8e5e714 kostenfrei https://www.frontiersin.org/articles/10.3389/fphy.2022.1058474/full kostenfrei https://doaj.org/toc/2296-424X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022
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container_title	Frontiers in Physics
authorswithroles_txt_mv	Danning Zhao @@aut@@ Yu Lei @@aut@@ Jinsong Xu @@aut@@ Hongbing Cai @@aut@@
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callnumber-first	Q - Science
author	Danning Zhao
spellingShingle	Danning Zhao misc QC1-999 misc multi-scale fuzzy entropy misc multi-scale dispersion entropy misc multi-scale permutation entropy misc multi-scale sample entropy misc marine ambient noise misc ship-radiated noise misc Physics A comparative study of four types of multi-scale entropies in feature extraction of underwater acoustic signals for potential GNSS positioning applications
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topic_title	QC1-999 A comparative study of four types of multi-scale entropies in feature extraction of underwater acoustic signals for potential GNSS positioning applications multi-scale fuzzy entropy multi-scale dispersion entropy multi-scale permutation entropy multi-scale sample entropy marine ambient noise ship-radiated noise
topic	misc QC1-999 misc multi-scale fuzzy entropy misc multi-scale dispersion entropy misc multi-scale permutation entropy misc multi-scale sample entropy misc marine ambient noise misc ship-radiated noise misc Physics
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title	A comparative study of four types of multi-scale entropies in feature extraction of underwater acoustic signals for potential GNSS positioning applications
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title_full	A comparative study of four types of multi-scale entropies in feature extraction of underwater acoustic signals for potential GNSS positioning applications
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title_sort	comparative study of four types of multi-scale entropies in feature extraction of underwater acoustic signals for potential gnss positioning applications
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title_auth	A comparative study of four types of multi-scale entropies in feature extraction of underwater acoustic signals for potential GNSS positioning applications
abstract	The combination of underwater acoustic processing and the Global Navigation Satellite System (GNSS) has achieved remarkable economic benefits in offshore operations. As the key technology of GNNS positioning, feature extraction of underwater acoustic signals is affected by the complex marine environment. To extract more effective information from underwater acoustic signals, we use four types of multi-scale entropies, including multi-scale sample entropy (MSE), multi-scale fuzzy entropy (MFE), multi-scale permutation entropy (MPE), and multi-scale dispersion entropy (MDE), to analyze and distinguish underwater acoustic signals. In this study, two groups of real-word underwater acoustic signal experiments were performed for feature extraction of ship-radiated noises (SRNs) and ambient noises (ANs). The results indicated that the performance of the MFE-based feature extraction method is superior to that of feature extraction methods based on the other three entropies under the same number of features, and the highest average recognition rate (ARR) of the MFE-based feature extraction method for SRNs reaches 100% when the number of features is 3.
abstractGer	The combination of underwater acoustic processing and the Global Navigation Satellite System (GNSS) has achieved remarkable economic benefits in offshore operations. As the key technology of GNNS positioning, feature extraction of underwater acoustic signals is affected by the complex marine environment. To extract more effective information from underwater acoustic signals, we use four types of multi-scale entropies, including multi-scale sample entropy (MSE), multi-scale fuzzy entropy (MFE), multi-scale permutation entropy (MPE), and multi-scale dispersion entropy (MDE), to analyze and distinguish underwater acoustic signals. In this study, two groups of real-word underwater acoustic signal experiments were performed for feature extraction of ship-radiated noises (SRNs) and ambient noises (ANs). The results indicated that the performance of the MFE-based feature extraction method is superior to that of feature extraction methods based on the other three entropies under the same number of features, and the highest average recognition rate (ARR) of the MFE-based feature extraction method for SRNs reaches 100% when the number of features is 3.
abstract_unstemmed	The combination of underwater acoustic processing and the Global Navigation Satellite System (GNSS) has achieved remarkable economic benefits in offshore operations. As the key technology of GNNS positioning, feature extraction of underwater acoustic signals is affected by the complex marine environment. To extract more effective information from underwater acoustic signals, we use four types of multi-scale entropies, including multi-scale sample entropy (MSE), multi-scale fuzzy entropy (MFE), multi-scale permutation entropy (MPE), and multi-scale dispersion entropy (MDE), to analyze and distinguish underwater acoustic signals. In this study, two groups of real-word underwater acoustic signal experiments were performed for feature extraction of ship-radiated noises (SRNs) and ambient noises (ANs). The results indicated that the performance of the MFE-based feature extraction method is superior to that of feature extraction methods based on the other three entropies under the same number of features, and the highest average recognition rate (ARR) of the MFE-based feature extraction method for SRNs reaches 100% when the number of features is 3.
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title_short	A comparative study of four types of multi-scale entropies in feature extraction of underwater acoustic signals for potential GNSS positioning applications
url	https://doi.org/10.3389/fphy.2022.1058474 https://doaj.org/article/9cc9571135d545e38f2079d7f8e5e714 https://www.frontiersin.org/articles/10.3389/fphy.2022.1058474/full https://doaj.org/toc/2296-424X
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A comparative study of four types of multi-scale entropies in feature extraction of underwater acoustic signals for potential GNSS positioning applications

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