Research on Feature Extraction of Ship-Radiated Noise Based on Compressed Sensing and Center Frequency
In the field of underwater acoustic signal processing, the ship radiated noise contains a large amount of ship information, which is of great significance to the ship identification. The traditional method relies too much on the operator and prior knowledge, which seriously reduces the efficiency an...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9539232/ |
| _version_ | 1819132184390270976 |
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| author | Zhufeng Lei Xiaofang Lei Chuanghui Zhou Lyujun Qing Qingyang Zhang Wenxiong Chao |
| author_facet | Zhufeng Lei Xiaofang Lei Chuanghui Zhou Lyujun Qing Qingyang Zhang Wenxiong Chao |
| author_sort | Zhufeng Lei |
| collection | DOAJ |
| description | In the field of underwater acoustic signal processing, the ship radiated noise contains a large amount of ship information, which is of great significance to the ship identification. The traditional method relies too much on the operator and prior knowledge, which seriously reduces the efficiency and accuracy of the ship radiated noise identification. This paper presented a novel ship radiated noise feature extraction method based on compression sensing and center frequency. Firstly, to compression sensing of the ship radiated noise, enhance its line spectrum energy. Then, the ship radiated noise is decomposed by empirical mode decomposition to obtain multiple intrinsic mode function, calculate the mutual information entropy of adjacent intrinsic mode function to determine the key parameter K of the variational mode decomposition. Finally, perform variational model of ship radiated noise based on K, extract the center frequency of maximum energy intrinsic mode function as the ship radiated noise recognition feature. Experimental results show that the proposed feature extraction method can classify ship radiated noise quickly and effectively, and reduce the dependence on operators and prior knowledge. |
| first_indexed | 2024-12-22T09:27:22Z |
| format | Article |
| id | doaj.art-3af540e284944118a6294678fd6ec41b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T09:27:22Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3af540e284944118a6294678fd6ec41b2022-12-21T18:31:03ZengIEEEIEEE Access2169-35362021-01-01912867912868610.1109/ACCESS.2021.31130429539232Research on Feature Extraction of Ship-Radiated Noise Based on Compressed Sensing and Center FrequencyZhufeng Lei0https://orcid.org/0000-0003-3408-1315Xiaofang Lei1Chuanghui Zhou2Lyujun Qing3Qingyang Zhang4Wenxiong Chao5National Joint Engineering Research Center for Special Pump Technology, Xi’an Aeronautical University, Xi’an, Shaanxi, ChinaChina National Heavy Machinery Research Institute Company Ltd, Xi’an, ChinaNational Joint Engineering Research Center for Special Pump Technology, Xi’an Aeronautical University, Xi’an, Shaanxi, ChinaNational Joint Engineering Research Center for Special Pump Technology, Xi’an Aeronautical University, Xi’an, Shaanxi, ChinaNational Joint Engineering Research Center for Special Pump Technology, Xi’an Aeronautical University, Xi’an, Shaanxi, ChinaNational Joint Engineering Research Center for Special Pump Technology, Xi’an Aeronautical University, Xi’an, Shaanxi, ChinaIn the field of underwater acoustic signal processing, the ship radiated noise contains a large amount of ship information, which is of great significance to the ship identification. The traditional method relies too much on the operator and prior knowledge, which seriously reduces the efficiency and accuracy of the ship radiated noise identification. This paper presented a novel ship radiated noise feature extraction method based on compression sensing and center frequency. Firstly, to compression sensing of the ship radiated noise, enhance its line spectrum energy. Then, the ship radiated noise is decomposed by empirical mode decomposition to obtain multiple intrinsic mode function, calculate the mutual information entropy of adjacent intrinsic mode function to determine the key parameter K of the variational mode decomposition. Finally, perform variational model of ship radiated noise based on K, extract the center frequency of maximum energy intrinsic mode function as the ship radiated noise recognition feature. Experimental results show that the proposed feature extraction method can classify ship radiated noise quickly and effectively, and reduce the dependence on operators and prior knowledge.https://ieeexplore.ieee.org/document/9539232/Ship radiated noisecompressed sensingempirical mode decompositionvariational mode decompositioncenter frequency |
| spellingShingle | Zhufeng Lei Xiaofang Lei Chuanghui Zhou Lyujun Qing Qingyang Zhang Wenxiong Chao Research on Feature Extraction of Ship-Radiated Noise Based on Compressed Sensing and Center Frequency IEEE Access Ship radiated noise compressed sensing empirical mode decomposition variational mode decomposition center frequency |
| title | Research on Feature Extraction of Ship-Radiated Noise Based on Compressed Sensing and Center Frequency |
| title_full | Research on Feature Extraction of Ship-Radiated Noise Based on Compressed Sensing and Center Frequency |
| title_fullStr | Research on Feature Extraction of Ship-Radiated Noise Based on Compressed Sensing and Center Frequency |
| title_full_unstemmed | Research on Feature Extraction of Ship-Radiated Noise Based on Compressed Sensing and Center Frequency |
| title_short | Research on Feature Extraction of Ship-Radiated Noise Based on Compressed Sensing and Center Frequency |
| title_sort | research on feature extraction of ship radiated noise based on compressed sensing and center frequency |
| topic | Ship radiated noise compressed sensing empirical mode decomposition variational mode decomposition center frequency |
| url | https://ieeexplore.ieee.org/document/9539232/ |
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