Multi-Channel Underwater Acoustic Signal Analysis Using Improved Multivariate Multiscale Sample Entropy
Underwater acoustic signals typically exhibit non-Gaussian, non-stationary, and nonlinear characteristics. When processing real-world underwater acoustic signals, traditional multivariate entropy algorithms often struggle to simultaneously ensure stability and extract cross-channel information. To a...
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MDPI AG
2025-03-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/4/675 |
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| author | Jing Zhou Yaan Li Mingzhou Wang |
| author_facet | Jing Zhou Yaan Li Mingzhou Wang |
| author_sort | Jing Zhou |
| collection | DOAJ |
| description | Underwater acoustic signals typically exhibit non-Gaussian, non-stationary, and nonlinear characteristics. When processing real-world underwater acoustic signals, traditional multivariate entropy algorithms often struggle to simultaneously ensure stability and extract cross-channel information. To address these issues, the improved multivariate multiscale sample entropy (IMMSE) algorithm is proposed, which extracts the complexity of multi-channel data, enabling a more comprehensive and stable representation of the dynamic characteristics of complex nonlinear systems. This paper explores the optimal parameter selection range for the IMMSE algorithm and compares its sensitivity to noise and computational efficiency with traditional multivariate entropy algorithms. The results demonstrate that IMMSE outperforms its counterparts in terms of both stability and computational efficiency. Analysis of various types of ship-radiated noise further demonstrates IMMSE’s superior stability in handling complex underwater acoustic signals. Moreover, IMMSE’s ability to extract features enables more accurate discrimination between different signal types. Finally, the paper presents data processing results in mechanical fault diagnosis, underscoring the broad applicability of IMMSE. |
| format | Article |
| id | doaj-art-a8c24a9ce3f44a7ea3b29341b8317f7a |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-a8c24a9ce3f44a7ea3b29341b8317f7a2025-08-20T02:18:16ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-03-0113467510.3390/jmse13040675Multi-Channel Underwater Acoustic Signal Analysis Using Improved Multivariate Multiscale Sample EntropyJing Zhou0Yaan Li1Mingzhou Wang2School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaXi’an Precision Machinery Research Institute, National Key Laboratory of Underwater Information and Control, Xi’an 710077, ChinaUnderwater acoustic signals typically exhibit non-Gaussian, non-stationary, and nonlinear characteristics. When processing real-world underwater acoustic signals, traditional multivariate entropy algorithms often struggle to simultaneously ensure stability and extract cross-channel information. To address these issues, the improved multivariate multiscale sample entropy (IMMSE) algorithm is proposed, which extracts the complexity of multi-channel data, enabling a more comprehensive and stable representation of the dynamic characteristics of complex nonlinear systems. This paper explores the optimal parameter selection range for the IMMSE algorithm and compares its sensitivity to noise and computational efficiency with traditional multivariate entropy algorithms. The results demonstrate that IMMSE outperforms its counterparts in terms of both stability and computational efficiency. Analysis of various types of ship-radiated noise further demonstrates IMMSE’s superior stability in handling complex underwater acoustic signals. Moreover, IMMSE’s ability to extract features enables more accurate discrimination between different signal types. Finally, the paper presents data processing results in mechanical fault diagnosis, underscoring the broad applicability of IMMSE.https://www.mdpi.com/2077-1312/13/4/675improved multivariate multiscale sample entropy (IMMSE)cross-channel informationmultivariate system complexity |
| spellingShingle | Jing Zhou Yaan Li Mingzhou Wang Multi-Channel Underwater Acoustic Signal Analysis Using Improved Multivariate Multiscale Sample Entropy Journal of Marine Science and Engineering improved multivariate multiscale sample entropy (IMMSE) cross-channel information multivariate system complexity |
| title | Multi-Channel Underwater Acoustic Signal Analysis Using Improved Multivariate Multiscale Sample Entropy |
| title_full | Multi-Channel Underwater Acoustic Signal Analysis Using Improved Multivariate Multiscale Sample Entropy |
| title_fullStr | Multi-Channel Underwater Acoustic Signal Analysis Using Improved Multivariate Multiscale Sample Entropy |
| title_full_unstemmed | Multi-Channel Underwater Acoustic Signal Analysis Using Improved Multivariate Multiscale Sample Entropy |
| title_short | Multi-Channel Underwater Acoustic Signal Analysis Using Improved Multivariate Multiscale Sample Entropy |
| title_sort | multi channel underwater acoustic signal analysis using improved multivariate multiscale sample entropy |
| topic | improved multivariate multiscale sample entropy (IMMSE) cross-channel information multivariate system complexity |
| url | https://www.mdpi.com/2077-1312/13/4/675 |
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