A Method for Extracting Features of the Intrinsic Mode Function’s Energy Arrangement Entropy in the Shaft Frequency Electric Field of Vessels
To address the challenge of detecting low-frequency electric field signals from vessels in complex marine environments, a vessel shaft frequency electric field feature extraction method based on intrinsic mode function energy arrangement entropy values is proposed, building upon a scaled model. This...
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| author | Xiaoguang Ma Zhaolong Sun Runxiang Jiang Xinquan Yue Qi Liu |
| author_facet | Xiaoguang Ma Zhaolong Sun Runxiang Jiang Xinquan Yue Qi Liu |
| author_sort | Xiaoguang Ma |
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| description | To address the challenge of detecting low-frequency electric field signals from vessels in complex marine environments, a vessel shaft frequency electric field feature extraction method based on intrinsic mode function energy arrangement entropy values is proposed, building upon a scaled model. This study initially establishes a measurement system for shaft frequency electric fields, utilizing a titanium-based oxide electrode to construct an equivalent dipole source simulating the shaft frequency electric field signals of different types of vessels. Subsequently, a comparative analysis of the time-domain and frequency-domain characteristics of signals after modal decomposition is conducted. A feature extraction method is then proposed that combines the maximum average energy of intrinsic mode functions with arrangement entropy values to achieve discrimination of target signals. Finally, the feasibility of the proposed method is validated through sea trials. The results indicate that the method can successfully screen different types of typical vessels and address the target screening failure caused by slight differences in the characteristic parameters of the shaft frequency electric field signal. The entropy difference has been improved from 0.05 to about 0.2, and the difference rate of the shaft frequency electric field signal has been improved by 75%. This has effectively reduced the false alarm rate of target detection. |
| format | Article |
| id | doaj-art-90c8f9d79913404b9bfee8350317457a |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-90c8f9d79913404b9bfee8350317457a2025-08-20T02:23:06ZengMDPI AGApplied Sciences2076-34172025-05-011511614310.3390/app15116143A Method for Extracting Features of the Intrinsic Mode Function’s Energy Arrangement Entropy in the Shaft Frequency Electric Field of VesselsXiaoguang Ma0Zhaolong Sun1Runxiang Jiang2Xinquan Yue3Qi Liu4College of Electrical Engineering, Naval University of Engineering, Wuhan 430033, ChinaCollege of Electrical Engineering, Naval University of Engineering, Wuhan 430033, ChinaCollege of Electrical Engineering, Naval University of Engineering, Wuhan 430033, ChinaCollege of Electrical Engineering, Naval University of Engineering, Wuhan 430033, ChinaPLA Naval Submarine Academy, Qingdao 266000, ChinaTo address the challenge of detecting low-frequency electric field signals from vessels in complex marine environments, a vessel shaft frequency electric field feature extraction method based on intrinsic mode function energy arrangement entropy values is proposed, building upon a scaled model. This study initially establishes a measurement system for shaft frequency electric fields, utilizing a titanium-based oxide electrode to construct an equivalent dipole source simulating the shaft frequency electric field signals of different types of vessels. Subsequently, a comparative analysis of the time-domain and frequency-domain characteristics of signals after modal decomposition is conducted. A feature extraction method is then proposed that combines the maximum average energy of intrinsic mode functions with arrangement entropy values to achieve discrimination of target signals. Finally, the feasibility of the proposed method is validated through sea trials. The results indicate that the method can successfully screen different types of typical vessels and address the target screening failure caused by slight differences in the characteristic parameters of the shaft frequency electric field signal. The entropy difference has been improved from 0.05 to about 0.2, and the difference rate of the shaft frequency electric field signal has been improved by 75%. This has effectively reduced the false alarm rate of target detection.https://www.mdpi.com/2076-3417/15/11/6143shaft frequency electric field of vesselintrinsic mode functionmaximum average energyarrangement entropyfeature extraction |
| spellingShingle | Xiaoguang Ma Zhaolong Sun Runxiang Jiang Xinquan Yue Qi Liu A Method for Extracting Features of the Intrinsic Mode Function’s Energy Arrangement Entropy in the Shaft Frequency Electric Field of Vessels Applied Sciences shaft frequency electric field of vessel intrinsic mode function maximum average energy arrangement entropy feature extraction |
| title | A Method for Extracting Features of the Intrinsic Mode Function’s Energy Arrangement Entropy in the Shaft Frequency Electric Field of Vessels |
| title_full | A Method for Extracting Features of the Intrinsic Mode Function’s Energy Arrangement Entropy in the Shaft Frequency Electric Field of Vessels |
| title_fullStr | A Method for Extracting Features of the Intrinsic Mode Function’s Energy Arrangement Entropy in the Shaft Frequency Electric Field of Vessels |
| title_full_unstemmed | A Method for Extracting Features of the Intrinsic Mode Function’s Energy Arrangement Entropy in the Shaft Frequency Electric Field of Vessels |
| title_short | A Method for Extracting Features of the Intrinsic Mode Function’s Energy Arrangement Entropy in the Shaft Frequency Electric Field of Vessels |
| title_sort | method for extracting features of the intrinsic mode function s energy arrangement entropy in the shaft frequency electric field of vessels |
| topic | shaft frequency electric field of vessel intrinsic mode function maximum average energy arrangement entropy feature extraction |
| url | https://www.mdpi.com/2076-3417/15/11/6143 |
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