Underwater Acoustic Target Recognition in Passive Sonar Using Spectrogram and Modified MobileNet Network Classifier

Underwater Acoustic Target Recognition in Passive Sonar Using Spectrogram and Modified MobileNet Network Classifier | 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 Research Article Underwater Acoustic Target Recognition in Passive Sonar Using Spectrogram and Modified MobileNet Network Classifier Hassan Akbarian, Mohammad hosein Sedaaghi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3758160/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 When the surface and subsurface floats move in the water, they emit sounds due to their propulsion engines as well as the rotation of their propellers. One of the best methods in underwater automatic target recognition (UATR) is to use deep learning to extract features and supervised train acoustic datasets that are used in the world’s naval forces. In this article, to achieve reliable results by deep learning methods, we collected the raw acoustic signals received by the hydrophones in the relevant database with the label of each class, and we performed the necessary pre-processing on them so that they become a stationary signal and finally provided them to the spectrogram system. Next, by using short-term frequency transformation (STFT), the spectrogram of high resonance components is obtained and used as the input of the modified MobileNet classifier for model training and evaluation. The simulation results with the Python program indicate that the suggested technique can reach a classification accuracy of 97.37% and a validation loss of less than 3%. In this research, a model has been proposed that, in addition to reducing complexity, has achieved a good balance between classification accuracy and speed. Deep Learning Passive Sonar Spectrogram Underwater Acoustic Target Recognition MobileNet Full Text 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-3758160","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":259947808,"identity":"6b890c3d-c013-41a4-9045-c0d1cafd363b","order_by":0,"name":"Hassan Akbarian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYDACHh4gYQDEEhC+HIg48IAULcZgLQkEtTAgtCQ2gEh8Wvh7zh78XFFgl8cg3fz444+aO+nzww4/BNpiJ6fbgF2LxNm+ZMkzBsnFDDLHzKR5jj3L3Xg7zQCoJdnY7AAOa87zGEg2GDAnNkgkmDEzsB3O3Tg7AaTlQOI2HFrkz/MY/2wwqAdqSf/88ce/w+mGs9M/4NVicLbHDGjLYaCWHAMJ3rbDCfLSOfhtMTxzLs2yweB4YptETpk0b99hww3SOQUHEgxw+0XuTO7hmw1/qhP7JdI3f/zx7bC8/Oz0zR8+VNjJ4fQ+DLDBnQpWaUBAOQqQbyBF9SgYBaNgFIwEAABNW2LEmn8cwwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-6022-9806","institution":"sahand university of technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hassan","middleName":"","lastName":"Akbarian","suffix":""},{"id":259948019,"identity":"7b4e6fe5-422a-4771-934f-0dffbeb5b299","order_by":1,"name":"Mohammad hosein Sedaaghi","email":"","orcid":"https://orcid.org/0000-0001-6937-9176","institution":"sahand university of technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"hosein","lastName":"Sedaaghi","suffix":""}],"badges":[],"createdAt":"2023-12-15 10:43:09","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-3758160/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3758160/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":48372875,"identity":"c605c8ad-e14e-4cab-89d6-c7df9f23e792","added_by":"auto","created_at":"2023-12-18 07:00:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1249109,"visible":true,"origin":"","legend":"","description":"","filename":"PaperUATRSpectrogramandModifiedMobileNet.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3758160/v1_covered_45c7ded0-6418-4851-a6c3-90074cfeeb38.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eUnderwater Acoustic Target Recognition in Passive Sonar Using Spectrogram and Modified MobileNet Network Classifier\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Sahand University of Technology","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":"Deep Learning, Passive Sonar, Spectrogram, Underwater Acoustic Target Recognition, MobileNet","lastPublishedDoi":"10.21203/rs.3.rs-3758160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3758160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhen the surface and subsurface floats move in the water, they emit sounds due to their propulsion engines as well as the rotation of their propellers. One of the best methods in underwater automatic target recognition (UATR) is to use deep learning to extract features and supervised train acoustic datasets that are used in the world’s naval forces. In this article, to achieve reliable results by deep learning methods, we collected the raw acoustic signals received by the hydrophones in the relevant database with the label of each class, and we performed the necessary pre-processing on them so that they become a stationary signal and finally provided them to the spectrogram system. Next, by using short-term frequency transformation (STFT), the spectrogram of high resonance components is obtained and used as the input of the modified MobileNet classifier for model training and evaluation. The simulation results with the Python program indicate that the suggested technique can reach a classification accuracy of 97.37% and a validation loss of less than 3%. 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