Underwater Data Mapping and Vessel Localization Using Multi-Sensor Complex Based on Gaussian Process and Deep Learning

This research proposes a novel method for determining the location of vessels using a multi-sensor complex consisting of sound, magnetic, and depth sensors. The goal is to create a reliable and precise system that can overcome the limitations of individual sensors by integrating their data. Gaussian Process Regression (GPR) is employed to interpolate sparse data collected by the multi-sensor system, while a deep learning model based on the Transformer architecture is used to estimate the vessel's position. The system is designed to enhance accuracy and robustness, particularly in noisy marine environments. Our method shows potential for real-time applications in underwater localization, particularly in areas with high noise and interference.