Underwater Localization Using SAR Satellite Data

This study delves into the realm of Underwater Wireless Sensor Networks (UWSN) and explores contemporary methods of ocean exploration. It provides an extensive background on UWSN, detailing existing approaches to underwater localization. The study then introduces a novel contribution to this domain by leveraging advanced satellite technology. Employing a pre-trained deep learning model from ArcGIS, static ships within the study area are identified using C-band Synthetic Aperture Radar (SAR) satellite imagery. The identified ship locations serve as reference nodes for underwater localization. Utilizing range-based multilateration in the UnetStack environment, the study achieves precise localization of underwater nodes. The proposed approach demonstrates an error of less than 1% when compared to the actual positions of the underwater nodes, showcasing its effectiveness in enhancing the field of underwater exploration and localization.