Underwater acoustic target recognition under working conditions mismatch
The working conditions of the ship will have a great impact on the radiated noise of the ship. Even if the same ship is traveling in the same sea area, different working conditions will produce different radiated noise, thus affecting the accuracy of target recognition. Especially in the case of wor...
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| Format: | Article |
| Language: | zho |
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EDP Sciences
2024-12-01
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| Series: | Xibei Gongye Daxue Xuebao |
| Subjects: | |
| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2024/06/jnwpu2024426p1039/jnwpu2024426p1039.html |
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| _version_ | 1825206576720379904 |
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| author | WANG Haitao JIN Anqi YANG Shuang ZENG Xiangyang |
| author_facet | WANG Haitao JIN Anqi YANG Shuang ZENG Xiangyang |
| author_sort | WANG Haitao |
| collection | DOAJ |
| description | The working conditions of the ship will have a great impact on the radiated noise of the ship. Even if the same ship is traveling in the same sea area, different working conditions will produce different radiated noise, thus affecting the accuracy of target recognition. Especially in the case of working condition mismatch, the correct rate of the recognition results will be greatly reduced. To address this problem, an intelligent underwater acoustic target recognition method based on knowledge distillation is proposed to improve the recognition accuracy. Auditory features are used as inputs to the system, and knowledge distillation is utilized to learn the intrinsic connection of target features under different working conditions. The teacher network, trained from a large amount of existing working condition data, is used to assist the student network (trained from a small amount of working condition data) to solve the working condition mismatch problem under different conditions. Tests were conducted using ship radiated noise datasets under four working conditions. The results show that the proposed method outperforms the other methods in all kinds of working condition mismatch problems, which demonstrates its intelligence and practicality in engineering problems. |
| format | Article |
| id | doaj-art-92f5a71748a740b8b3c1e8263bda4ec1 |
| institution | Kabale University |
| issn | 1000-2758 2609-7125 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Xibei Gongye Daxue Xuebao |
| spelling | doaj-art-92f5a71748a740b8b3c1e8263bda4ec12025-02-07T08:23:13ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252024-12-014261039104610.1051/jnwpu/20244261039jnwpu2024426p1039Underwater acoustic target recognition under working conditions mismatchWANG Haitao0JIN Anqi1YANG Shuang2ZENG Xiangyang3School of Marine Science and Technology, Northwestern Polytechnical UniversitySchool of Marine Science and Technology, Northwestern Polytechnical UniversitySchool of Marine Science and Technology, Northwestern Polytechnical UniversitySchool of Marine Science and Technology, Northwestern Polytechnical UniversityThe working conditions of the ship will have a great impact on the radiated noise of the ship. Even if the same ship is traveling in the same sea area, different working conditions will produce different radiated noise, thus affecting the accuracy of target recognition. Especially in the case of working condition mismatch, the correct rate of the recognition results will be greatly reduced. To address this problem, an intelligent underwater acoustic target recognition method based on knowledge distillation is proposed to improve the recognition accuracy. Auditory features are used as inputs to the system, and knowledge distillation is utilized to learn the intrinsic connection of target features under different working conditions. The teacher network, trained from a large amount of existing working condition data, is used to assist the student network (trained from a small amount of working condition data) to solve the working condition mismatch problem under different conditions. Tests were conducted using ship radiated noise datasets under four working conditions. The results show that the proposed method outperforms the other methods in all kinds of working condition mismatch problems, which demonstrates its intelligence and practicality in engineering problems.https://www.jnwpu.org/articles/jnwpu/full_html/2024/06/jnwpu2024426p1039/jnwpu2024426p1039.htmlship radiated noiseknowledge distillationunderwater acoustic target recognitionworking condition mismatch |
| spellingShingle | WANG Haitao JIN Anqi YANG Shuang ZENG Xiangyang Underwater acoustic target recognition under working conditions mismatch Xibei Gongye Daxue Xuebao ship radiated noise knowledge distillation underwater acoustic target recognition working condition mismatch |
| title | Underwater acoustic target recognition under working conditions mismatch |
| title_full | Underwater acoustic target recognition under working conditions mismatch |
| title_fullStr | Underwater acoustic target recognition under working conditions mismatch |
| title_full_unstemmed | Underwater acoustic target recognition under working conditions mismatch |
| title_short | Underwater acoustic target recognition under working conditions mismatch |
| title_sort | underwater acoustic target recognition under working conditions mismatch |
| topic | ship radiated noise knowledge distillation underwater acoustic target recognition working condition mismatch |
| url | https://www.jnwpu.org/articles/jnwpu/full_html/2024/06/jnwpu2024426p1039/jnwpu2024426p1039.html |
| work_keys_str_mv | AT wanghaitao underwateracoustictargetrecognitionunderworkingconditionsmismatch AT jinanqi underwateracoustictargetrecognitionunderworkingconditionsmismatch AT yangshuang underwateracoustictargetrecognitionunderworkingconditionsmismatch AT zengxiangyang underwateracoustictargetrecognitionunderworkingconditionsmismatch |