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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2410.11223 |
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| _version_ | 1866912159563251712 |
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| author | Ding, Yimian Xu, Jingzehua Xie, Guanwen Wang, Haoyu Liu, Weiyi Li, Yi |
| author_facet | Ding, Yimian Xu, Jingzehua Xie, Guanwen Wang, Haoyu Liu, Weiyi Li, Yi |
| contents | Accurate underwater target localization is essential for underwater exploration. To improve accuracy and efficiency in complex underwater environments, we propose the Electric Field Inversion-Localization Network (EFILN), a deep feedforward neural network that reconstructs position coordinates from underwater electric field signals. By assessing whether the neural network's input-output values satisfy the Coulomb law, the error between the network's inversion solution and the equation's exact solution can be determined. The Adam optimizer was employed first, followed by the L-BFGS optimizer, to progressively improve the output precision of EFILN. A series of noise experiments demonstrated the robustness and practical utility of the proposed method, while small sample data experiments validated its strong small-sample learning (SSL) capabilities. To accelerate relevant research, we have made the codes available as open-source. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11223 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EFILN: The Electric Field Inversion-Localization Network for High-Precision Underwater Positioning Ding, Yimian Xu, Jingzehua Xie, Guanwen Wang, Haoyu Liu, Weiyi Li, Yi Systems and Control Accurate underwater target localization is essential for underwater exploration. To improve accuracy and efficiency in complex underwater environments, we propose the Electric Field Inversion-Localization Network (EFILN), a deep feedforward neural network that reconstructs position coordinates from underwater electric field signals. By assessing whether the neural network's input-output values satisfy the Coulomb law, the error between the network's inversion solution and the equation's exact solution can be determined. The Adam optimizer was employed first, followed by the L-BFGS optimizer, to progressively improve the output precision of EFILN. A series of noise experiments demonstrated the robustness and practical utility of the proposed method, while small sample data experiments validated its strong small-sample learning (SSL) capabilities. To accelerate relevant research, we have made the codes available as open-source. |
| title | EFILN: The Electric Field Inversion-Localization Network for High-Precision Underwater Positioning |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2410.11223 |