A Feature Extraction Method of Ship Underwater Noise Using Enhanced Peak Cross-Correlation Empirical Mode Decomposition Method and Multi-Scale Permutation Entropy
A feature extraction method based on the combination of improved empirical modal decomposition (IEMD) and multi-scale permutation entropy (MPE) is proposed to address the problem of inaccurate recognition and classification of ship noise signals under complex environmental conditions. In order to el...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/12/2222 |
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| author | Peng Liu Chen Dai Shuaiqiang Li Hui Jin Xinfu Liu Guijie Liu |
| author_facet | Peng Liu Chen Dai Shuaiqiang Li Hui Jin Xinfu Liu Guijie Liu |
| author_sort | Peng Liu |
| collection | DOAJ |
| description | A feature extraction method based on the combination of improved empirical modal decomposition (IEMD) and multi-scale permutation entropy (MPE) is proposed to address the problem of inaccurate recognition and classification of ship noise signals under complex environmental conditions. In order to eliminate the end effects, this paper proposes an extended model based on the principle of peak cross-correlation for improved empirical modal decomposition (EMD). In this paper, the IEMD method is used to decompose three ship underwater noise signals to extract the MPE features of the highest order intrinsic modal function (IMF) of energy. The results show that the IEMD-MPE method performs well in extracting the feature information of the signals and has a strong discriminative ability. Compared with the IEMD-aligned entropy (IEMD-PE) method, which describes the signals only at a single scale, the IEMD-MPE method achieves an improvement in the minimum difference distance ranging from 101.36% to 212.98%. In addition, two sets of highly similar ship propulsion noise signals were applied to validate the IEMD-MPE method, and the minimum differences of the experimental results were 0.0814 and 0.0057 entropy units, which verified the validity and generality of the method. This study provides theoretical support for the development of ship target recognition technology for propulsion. |
| format | Article |
| id | doaj-art-92947cd9a90749f3bcf462f45a7fb6a8 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-92947cd9a90749f3bcf462f45a7fb6a82024-12-27T14:33:18ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011212222210.3390/jmse12122222A Feature Extraction Method of Ship Underwater Noise Using Enhanced Peak Cross-Correlation Empirical Mode Decomposition Method and Multi-Scale Permutation EntropyPeng Liu0Chen Dai1Shuaiqiang Li2Hui Jin3Xinfu Liu4Guijie Liu5School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266525, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266525, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266525, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266525, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266525, ChinaCollege of Engineering, Ocean University of China, Qingdao 266005, ChinaA feature extraction method based on the combination of improved empirical modal decomposition (IEMD) and multi-scale permutation entropy (MPE) is proposed to address the problem of inaccurate recognition and classification of ship noise signals under complex environmental conditions. In order to eliminate the end effects, this paper proposes an extended model based on the principle of peak cross-correlation for improved empirical modal decomposition (EMD). In this paper, the IEMD method is used to decompose three ship underwater noise signals to extract the MPE features of the highest order intrinsic modal function (IMF) of energy. The results show that the IEMD-MPE method performs well in extracting the feature information of the signals and has a strong discriminative ability. Compared with the IEMD-aligned entropy (IEMD-PE) method, which describes the signals only at a single scale, the IEMD-MPE method achieves an improvement in the minimum difference distance ranging from 101.36% to 212.98%. In addition, two sets of highly similar ship propulsion noise signals were applied to validate the IEMD-MPE method, and the minimum differences of the experimental results were 0.0814 and 0.0057 entropy units, which verified the validity and generality of the method. This study provides theoretical support for the development of ship target recognition technology for propulsion.https://www.mdpi.com/2077-1312/12/12/2222ship underwater noise signalpeak cross-correlationEMDend effectsMPEfeature extraction |
| spellingShingle | Peng Liu Chen Dai Shuaiqiang Li Hui Jin Xinfu Liu Guijie Liu A Feature Extraction Method of Ship Underwater Noise Using Enhanced Peak Cross-Correlation Empirical Mode Decomposition Method and Multi-Scale Permutation Entropy Journal of Marine Science and Engineering ship underwater noise signal peak cross-correlation EMD end effects MPE feature extraction |
| title | A Feature Extraction Method of Ship Underwater Noise Using Enhanced Peak Cross-Correlation Empirical Mode Decomposition Method and Multi-Scale Permutation Entropy |
| title_full | A Feature Extraction Method of Ship Underwater Noise Using Enhanced Peak Cross-Correlation Empirical Mode Decomposition Method and Multi-Scale Permutation Entropy |
| title_fullStr | A Feature Extraction Method of Ship Underwater Noise Using Enhanced Peak Cross-Correlation Empirical Mode Decomposition Method and Multi-Scale Permutation Entropy |
| title_full_unstemmed | A Feature Extraction Method of Ship Underwater Noise Using Enhanced Peak Cross-Correlation Empirical Mode Decomposition Method and Multi-Scale Permutation Entropy |
| title_short | A Feature Extraction Method of Ship Underwater Noise Using Enhanced Peak Cross-Correlation Empirical Mode Decomposition Method and Multi-Scale Permutation Entropy |
| title_sort | feature extraction method of ship underwater noise using enhanced peak cross correlation empirical mode decomposition method and multi scale permutation entropy |
| topic | ship underwater noise signal peak cross-correlation EMD end effects MPE feature extraction |
| url | https://www.mdpi.com/2077-1312/12/12/2222 |
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