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

TL;DR

EFILN is a deep neural network designed for high-precision underwater localization by reconstructing positions from electric field signals, leveraging physics-based validation and optimization techniques.

Contribution

The paper introduces EFILN, a novel neural network architecture that combines physics-based validation with advanced optimization for improved underwater positioning accuracy.

Findings

EFILN achieves high accuracy in underwater localization.

The method demonstrates robustness against noise.

EFILN performs well with small sample data.

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.