A Multi-Target Electric Field Location Algorithm for Underwater Electrosense Robots Based on Sparse Bayesian Learning

Extremely Low Frequency (ELF) electric field signal provides a novel and promising solution to the target location problem due to its strong resistance to jamming and long propagation range. However, conventional algorithms such as Multiple Signal Classification (MUSIC) often rely heavily on accurate prior information. In this paper, we propose a novel underwater electric field location algorithm to accurately locate an unknown number of targets. This paper constructs a complete output model of electric field detection array in the spatial domain based on Sparse Bayesian Learning (SBL), and transforms the target location problem into sparse signal reconstruction problem. The experimental results demonstrate the effectiveness of the proposed method and its advantages over the MUSIC algorithm. The proposed location algorithm is capable of accurately locating an unknown number of ships and other targets.