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Underwater localization using SAR satellite data.

Muhammad, Aminu; Fough, Nazila; Kannan, Somasundar; Zahriban Hesari, Mozhgan

Authors

Aminu Muhammad

Mozhgan Zahriban Hesari



Abstract

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.

Citation

MUHAMMAD, A., FOUGH, N., KANNAN, S. and ZAHRIBAN HESARI, M. 2024. Underwater localization using SAR satellite data. In: Proceedings of the 2024 IEEE (Institute of Electrical and Electronics Engineers) International workshop on metrology for industry 4.0 and IoT (IEEE MetroInd4.0&IoT 2024), 29-31 May 2024, Florence, Italy. Piscataway: IEEE [online], pages 82-87. Available from: https://doi.org/10.1109/MetroInd4.0IoT61288.2024.10584174

Conference Name 2024 IEEE (Institute of Electrical and Electronics Engineers) International workshop on metrology for industry 4.0 and IoT (IEEE MetroInd4.0 & IoT 2024)
Conference Location Florence, Italy
Start Date May 29, 2024
End Date May 31, 2024
Acceptance Date Apr 7, 2024
Online Publication Date May 31, 2024
Publication Date Dec 31, 2024
Deposit Date May 17, 2024
Publicly Available Date Jul 18, 2024
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Pages 82-87
Series ISSN 2837-0872
DOI https://doi.org/10.1109/MetroInd4.0IoT61288.2024.10584174
Keywords Underwater localization; SAR data; Dynamic underwater localization
Public URL https://rgu-repository.worktribe.com/output/2339239

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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/

Copyright Statement
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.




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