A Classical method for determining the Sonar Contact Confidence Level for object detected by autonomus underwater vehicles

A Classical method for determining the Sonar Contact Confidence Level for object detected by autonomus underwater vehicles | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Classical method for determining the Sonar Contact Confidence Level for object detected by autonomus underwater vehicles Daniel Powarzyński, Szymon Labuda This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8127875/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Post-Mission Analysis (PMA) constitutes a critical stage in evaluating potential underwater threats within surveyed areas during missions conducted by Autonomous Underwater Vehicles (AUVs). In complex and dynamically changing acoustic environments, objects captured in side-scan sonar imagery are often subject to distortions that fail to accurately reflect their true geometry or dimensions. Such discrepancies may lead to misclassification and, consequently, the failure to identify hazardous objects such as naval mines, posing a significant threat to navigation safety and maritime operations. This study presents a methodological framework and a set of practical procedures for the classification of sonar-detected objects using the Sonar Contact Confidence Level (SCCL) approach, which enables quantitative assessment of contact confidence. The analysis is based on real sonar data collected during missions performed by advanced AUV platforms equipped with an EdgeTech 2205 side-scan sonar. The article provides a detailed characterization of both the underwater vehicles and the sonar system, followed by the description of real underwater objects and their corresponding SCCL levels. Furthermore, the study identifies and discusses key sources of classification uncertainty, highlighting environmental and instrumental factors contributing to erroneous classification decisions. The proposed framework offers practical insights aimed at enhancing interpretation reliability and improving the overall effectiveness of post-mission assessments, thereby contributing to the advancement of sonar-based object classification methodologies. Physical sciences/Engineering Physical sciences/Mathematics and computing Earth and environmental sciences/Ocean sciences Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8127875","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":555430994,"identity":"624d0c77-7e0e-44c5-924c-9e2d9d21abd2","order_by":0,"name":"Daniel Powarzyński","email":"data:image/png;base64,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","orcid":"","institution":"Polish Naval Academy","correspondingAuthor":true,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Powarzyński","suffix":""},{"id":555430995,"identity":"45c184de-4075-4e33-b4ef-fbbcd8553080","order_by":1,"name":"Szymon Labuda","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Szymon","middleName":"","lastName":"Labuda","suffix":""}],"badges":[],"createdAt":"2025-11-16 14:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8127875/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8127875/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97647352,"identity":"c6b88879-aac7-44f8-ba4a-43ac294d23e0","added_by":"auto","created_at":"2025-12-08 05:16:07","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5567,"visible":true,"origin":"","legend":"","description":"","filename":"b2b1b184923741b098b934697b0fe038.json","url":"https://assets-eu.researchsquare.com/files/rs-8127875/v1/b8bbe4adf791595f81e43794.json"},{"id":107091124,"identity":"d70f7ef4-6a8a-4dc4-b49c-54cfd9f8c314","added_by":"auto","created_at":"2026-04-16 15:56:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6232628,"visible":true,"origin":"","legend":"","description":"","filename":"ACLASSICMETHODFORDETERMININGTHESONARCONTACTCONFIDENCELEVELFOROBJECTDETECTEDBYAUTONOMUSUNDERWATERVEHICLESDPSL2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8127875/v1_covered_093eedab-0655-414c-8d77-0ba312aa6794.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Classical method for determining the Sonar Contact Confidence Level for object detected by autonomus underwater vehicles","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8127875/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8127875/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Post-Mission Analysis (PMA) constitutes a critical stage in evaluating potential underwater threats within surveyed areas during missions conducted by Autonomous Underwater Vehicles (AUVs). In complex and dynamically changing acoustic environments, objects captured in side-scan sonar imagery are often subject to distortions that fail to accurately reflect their true geometry or dimensions. Such discrepancies may lead to misclassification and, consequently, the failure to identify hazardous objects such as naval mines, posing a significant threat to navigation safety and maritime operations. This study presents a methodological framework and a set of practical procedures for the classification of sonar-detected objects using the Sonar Contact Confidence Level (SCCL) approach, which enables quantitative assessment of contact confidence. The analysis is based on real sonar data collected during missions performed by advanced AUV platforms equipped with an EdgeTech 2205 side-scan sonar. 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