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arXiv · 2410.11223

EFILN: The Electric Field Inversion-Localization Network for High-Precision Underwater Positioning

Yimian Ding·Jingzehua Xu·Guanwen Xie·Haoyu Wang·Weiyi Liu·Yi Li
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Abstract

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.

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Yimian Ding, Jingzehua Xu, Guanwen Xie, Haoyu Wang, Weiyi Liu, Yi Li. 2024-10-15. EFILN: The Electric Field Inversion-Localization Network for High-Precision Underwater Positioning. https://arxiv.org/abs/2410.11223

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