Evaluating the impact of ranging error in underwater localization using SAR satellite data.

Muhammad, Aminu; Stewart, Craig; Fough, Nazila; Kannan, Somasundar

Abstract

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.

Citation

MUHAMMAD, A., STEWART, C., FOUGH, N. and KANNAN, S. 2024. Evaluating the impact of ranging error in underwater localization using SAR satellite data. In Proceedings of the 2024 IEEE (Institute of Electrical and Electronics Engineers) International workshop on Metrology for the sea; learning to measure sea health parameters (IEEE MetroSea 2024), 14-16 October 2024, Portorose, Slovenia. Piscataway: IEEE [online], pages 40-45. Available from: https://doi.org/10.1109/MetroSea62823.2024.10765665

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 IEEE (Institute of Electrical and Electronics Engineers) International workshop on Metrology for the sea; learning to measure sea health parameters (IEEE MetroSea 2024)
• Start Date: Oct 14, 2024
• End Date: Oct 16, 2024
• Acceptance Date: Jul 13, 2024
• Online Publication Date: Oct 14, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Dec 6, 2024
• Publicly Available Date: Dec 20, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Pages: 40-45
• ISBN: 9798350379006
• DOI: https://doi.org/10.1109/metrosea62823.2024.10765665
• Keywords: Underwater localization; SAR satellite data; Optimization; Deep learning; Unetstack; Multilateration; Underwater wireless sensor networks (UWSN)
• Public URL: https://rgu-repository.worktribe.com/output/2613812