Evaluating the Impact of Ranging Error in Underwater Localization Using SAR Satellite Data

The study evaluated the impact of ranging error in underwater localization processes using SAR satellite data within the framework of Underwater Wireless Sensor Networks (UWSN). By integrating a pretrained ArcGIS deep learning model with SAR imagery, the study identifies static ships as reference nodes in a Scottish harbour, enabling precise localization of underwater nodes through range-based multilateration techniques in the Unetstack simulation environment. The study explores the impact of ranging errors on localization accuracy and optimizes node positioning to minimize the impact of these errors, demonstrating substantial improvements in the reliability of underwater node localization. This paper not only highlights the application of SAR data in enhancing underwater exploration but also sets a benchmark for future advancements in UWSN.