WUSTCA:Enhanced UAV RF Signal Classification with Wavelet Transform and STCA Attention Mechanisms

WUSTCA:Enhanced UAV RF Signal Classification with Wavelet Transform and STCA Attention Mechanisms | 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 WUSTCA:Enhanced UAV RF Signal Classification with Wavelet Transform and STCA Attention Mechanisms Jiyu Liu, Zaolin Xia, Yuhui Chen, Hanning Sun, Shu Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8534661/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Unmanned Aerial Vehicles (UAVs) play a vital role in various civilian and commercial applications, necessitating accurate classification of their radio frequency (RF) signals. Current deep learning-based approaches struggle with high computational complexity, susceptibility to noise, and limited accuracy. This paper introduces a novel UAV signal classification framework that combines wavelet-based feature extraction with a hierarchical U-Net architecture, enhanced by Split-Time Cross Attention (STCA) and residual connectivity. The WUSTCA model effectively classifies UAV and controller signals, achieving an average classification accuracy of 96.6% for UAVs and 95.83% for UAV controllers on the CardRF dataset. By addressing challenges such as noise interference and signal diversity, this work provides a reliable and efficient solution for UAV signal classification, paving the way for real-time applications in complex environments. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Feb, 2026 Reviews received at journal 18 Feb, 2026 Reviews received at journal 18 Feb, 2026 Reviews received at journal 28 Jan, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers agreed at journal 25 Jan, 2026 Reviewers agreed at journal 23 Jan, 2026 Reviewers invited by journal 22 Jan, 2026 Editor invited by journal 22 Jan, 2026 Editor assigned by journal 19 Jan, 2026 Submission checks completed at journal 19 Jan, 2026 First submitted to journal 06 Jan, 2026 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. 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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-8534661","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":580273594,"identity":"2e3a55e0-009e-469e-829e-8680ff450963","order_by":0,"name":"Jiyu Liu","email":"","orcid":"","institution":"Shangqiu Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jiyu","middleName":"","lastName":"Liu","suffix":""},{"id":580273595,"identity":"e4f3371e-e770-4d84-94be-2feca4ad0164","order_by":1,"name":"Zaolin Xia","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Zaolin","middleName":"","lastName":"Xia","suffix":""},{"id":580273596,"identity":"41fcbfd4-f9d0-4085-9908-c4e3e447c9f2","order_by":2,"name":"Yuhui Chen","email":"","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":false,"prefix":"","firstName":"Yuhui","middleName":"","lastName":"Chen","suffix":""},{"id":580273597,"identity":"c43e875f-78e9-43b0-a877-8ee81eb82760","order_by":3,"name":"Hanning Sun","email":"","orcid":"","institution":"Shenyang Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"Hanning","middleName":"","lastName":"Sun","suffix":""},{"id":580273598,"identity":"27b14cab-7e18-41e8-b455-4d18b98b4566","order_by":4,"name":"Shu Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACNobDBx9IVNjUN7Y3EKmFj/FYsoHFmTTG5p4DRGqRYz5jJlDZcpixfUYCsQ5jO2PGcLPhMDPvzMcbbzDU2EQT1sJzrOzhzB3pbJKz04otGI6l5TYQ1CJxeLux5BlrHsPZOWYSjA2HidAi/8BM+m8bs4T9zTPEamE4YiYh2eZswDiDh2gtwECWOJOWwNgD9EsCMX6Rb4BEZQJj++GNNz7U2BDWggwMJBJIUQ7RQqqOUTAKRsEoGBkAAEMJQmtftDAkAAAAAElFTkSuQmCC","orcid":"","institution":"Wuhan Vocational College of Software and Engineering(Wuhan Open University)","correspondingAuthor":true,"prefix":"","firstName":"Shu","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2026-01-06 19:53:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8534661/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8534661/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101296975,"identity":"e77f9be4-53b2-46f0-9ef4-ea7fe8758c65","added_by":"auto","created_at":"2026-01-28 09:24:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":843353,"visible":true,"origin":"","legend":"","description":"","filename":"TemplateforsubmissionstoScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8534661/v1_covered_060374f1-7b5d-402b-b5e6-06aad23a58d2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"WUSTCA:Enhanced UAV RF Signal Classification with Wavelet Transform and STCA Attention Mechanisms","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8534661/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8534661/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Unmanned Aerial Vehicles (UAVs) play a vital role in various civilian and commercial applications, necessitating accurate classification of their radio frequency (RF) signals. 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