EFILN: The Electric Field Inversion-Localization Network for High-Precision Underwater Positioning
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.
Keywords
Cite
@article{arxiv.2410.11223,
title = {EFILN: The Electric Field Inversion-Localization Network for High-Precision Underwater Positioning},
author = {Yimian Ding and Jingzehua Xu and Guanwen Xie and Haoyu Wang and Weiyi Liu and Yi Li},
journal= {arXiv preprint arXiv:2410.11223},
year = {2024}
}