Source detection and tracking for underwater distributed acoustic sensing

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dc.contributor.authorDrylerakis, Konstantinos Theofilos
dc.contributor.authorBelal, Mohammad
dc.contributor.authorMestre, Rafael
dc.contributor.authorNorman, Timothy J.
dc.contributor.authorEvers, Christine
dc.date.accessioned2025-01-20T13:50:16Z
dc.date.available2025-01-20T13:50:16Z
dc.date.freetoread2025-01-20
dc.date.issued2024-11-13
dc.date.pubOnline2025-01-20
dc.description.abstractDistributed Optical Fiber Sensing (DOFS) transforms conventional fiber optic cables into an extensive network of continuous sensors. It achieves this by exploiting the spectral, polarization and/or phase sensitivity of the propagating light to measurands of temperature, strain, pressure, vibrations etc. To harness the novel capabilities of optical fibers to remotely capture, process and coherently analyze ambient vibration (e.g., acoustic) fields, it is crucial to address the challenges of the diversity of noise introduced in DOFS measurements, in particular, within the under-explored submarine environment. This research introduces a comprehensive workflow for the detection of active (uncontrolled) acoustic sources, comprised of successive denoising steps that deal with the distinctive properties of such environments. Leveraging the spatio-temporal density of DOFS measurements, we develop a method based on data covariances for the automatic extraction of features in an unsupervised manner, together with additional features introduced to distinguish active source signals from noise. Consequently, this work takes the denoising of underwater DOFS data one step further through the application of a tracking algorithm on real, novel submarine DOFS data, laying the foundation for broader applications of DOFS data analysis in marine environmental sensing and monitoring.
dc.description.conferencenameDefence and Security Doctoral Symposia 2024 (DSDS24)
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)
dc.description.sponsorshipThis work was supported by UK Research and Innovation Centre for Doctoral Training in Machine Intelligence for Nano-electronic Devices and Systems [EP/S024298/1] and the Defence Science and Technology Laboratory.
dc.identifier.citationDrylerakis KT, Belal M, Mestre R, et al., (2024) Source detection and tracking for underwater distributed acoustic sensing - Poster. DSDS24, Cranfield Defence and Security Doctoral Symposia 2024, 13-14 November 2024, STEAM Museum, Swindon, UK
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23404
dc.identifier.urihttps://doi.org/10.57996/cran.ceres-2713
dc.language.isoen
dc.publisherCranfield University Defence and Security
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectdistributed acoustic sensing
dc.subjectmachine learning
dc.titleSource detection and tracking for underwater distributed acoustic sensing
dc.typePoster
dcterms.coverageSTEAM Museum, Swindon, UK
dcterms.dateAccepted2024-09-20
dcterms.temporal.endDate14-Nov-2024
dcterms.temporal.startDate13-Nov-2024

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