Vessel Detection and Localization Using Distributed Acoustic Sensing in Submarine Optical Fiber Cables
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
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2025
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| author | Ramirez-Torres, Erick Eduardo Macias-Guarasa, Javier Pizarro-Perez, Daniel Tejedor, Javier Palazuelos-Cagigas, Sira Elena Vidal-Moreno, Pedro J. Martin-Lopez, Sonia Gonzalez-Herraez, Miguel Vanthillo, Roel |
| author_facet | Ramirez-Torres, Erick Eduardo Macias-Guarasa, Javier Pizarro-Perez, Daniel Tejedor, Javier Palazuelos-Cagigas, Sira Elena Vidal-Moreno, Pedro J. Martin-Lopez, Sonia Gonzalez-Herraez, Miguel Vanthillo, Roel |
| contents | Submarine cables play a critical role in global internet connectivity, energy transmission, and communication but remain vulnerable to accidental damage and sabotage. Recent incidents in the Baltic Sea highlighted the need for enhanced monitoring to protect this vital infrastructure. Traditional vessel detection methods, such as synthetic aperture radar, video surveillance, and multispectral satellite imagery, face limitations in real-time processing, adverse weather conditions, and coverage range. This paper explores Distributed Acoustic Sensing (DAS) as an alternative by repurposing submarine telecommunication cables as large-scale acoustic sensor arrays. DAS offers continuous real-time monitoring, operates independently of cooperative systems like the "Automatic Identification System" (AIS), being largely unaffected by lighting or weather conditions. However, existing research on DAS for vessel tracking is limited in scale and lacks validation under real-world conditions. To address these gaps, a general and systematic methodology is presented for vessel detection and distance estimation using DAS. Advanced machine learning models are applied to improve detection and localization accuracy in dynamic maritime environments. The approach is evaluated over a continuous ten-day period, covering diverse ship and operational conditions, representing one of the largest-scale DAS-based vessel monitoring studies to date, and for which we release the full evaluation dataset. Results demonstrate DAS as a practical tool for maritime surveillance, with an overall F1-score of over 90% in vessel detection, and a mean average error of 141 m for vessel distance estimation, bridging the gap between experimental research and real-world deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11614 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Vessel Detection and Localization Using Distributed Acoustic Sensing in Submarine Optical Fiber Cables Ramirez-Torres, Erick Eduardo Macias-Guarasa, Javier Pizarro-Perez, Daniel Tejedor, Javier Palazuelos-Cagigas, Sira Elena Vidal-Moreno, Pedro J. Martin-Lopez, Sonia Gonzalez-Herraez, Miguel Vanthillo, Roel Geophysics Submarine cables play a critical role in global internet connectivity, energy transmission, and communication but remain vulnerable to accidental damage and sabotage. Recent incidents in the Baltic Sea highlighted the need for enhanced monitoring to protect this vital infrastructure. Traditional vessel detection methods, such as synthetic aperture radar, video surveillance, and multispectral satellite imagery, face limitations in real-time processing, adverse weather conditions, and coverage range. This paper explores Distributed Acoustic Sensing (DAS) as an alternative by repurposing submarine telecommunication cables as large-scale acoustic sensor arrays. DAS offers continuous real-time monitoring, operates independently of cooperative systems like the "Automatic Identification System" (AIS), being largely unaffected by lighting or weather conditions. However, existing research on DAS for vessel tracking is limited in scale and lacks validation under real-world conditions. To address these gaps, a general and systematic methodology is presented for vessel detection and distance estimation using DAS. Advanced machine learning models are applied to improve detection and localization accuracy in dynamic maritime environments. The approach is evaluated over a continuous ten-day period, covering diverse ship and operational conditions, representing one of the largest-scale DAS-based vessel monitoring studies to date, and for which we release the full evaluation dataset. Results demonstrate DAS as a practical tool for maritime surveillance, with an overall F1-score of over 90% in vessel detection, and a mean average error of 141 m for vessel distance estimation, bridging the gap between experimental research and real-world deployment. |
| title | Vessel Detection and Localization Using Distributed Acoustic Sensing in Submarine Optical Fiber Cables |
| topic | Geophysics |
| url | https://arxiv.org/abs/2509.11614 |