An Effective RF-Based Solution for Drone Detection and Recognition Amid Noise, Bluetooth, and Wi-Fi Interference

An Effective RF-Based Solution for Drone Detection and Recognition Amid Noise, Bluetooth, and Wi-Fi Interference | 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 An Effective RF-Based Solution for Drone Detection and Recognition Amid Noise, Bluetooth, and Wi-Fi Interference Trong Thanh Nguyen, Le Cuong Nguyen, Thi-Thanh-Tan Nguyen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5827543/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jun, 2025 Read the published version in Signal, Image and Video Processing → Version 1 posted 5 You are reading this latest preprint version Abstract Recent advancements in drone technology have raised significant security concerns, making the classification of drone types for early warning systems increasingly vital. This paper presents an effective solution that combines frequency domain signal transformations with deep learning models to improve drone detection accuracy. Our approach consists of four main stages: i) RF Signal Acquisition; ii) Energy Detection and Wavelet Transformation; iii) Relevant Signal Identification and Noise Elimination; iv) Area Enhancement and Drone ClassificationWe evaluated our model using real-world and publicly available datasets. Results indicate that our method demonstrates strong noise tolerance and optimal performance across both testing sets. Drone Radio Frequency Signal transformation Spectrogram Images Detection Classification Raw IQ data ROI Training Testing Confusion matrix Accuracy Precision Recall F1-Score Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Jun, 2025 Read the published version in Signal, Image and Video Processing → Version 1 posted Editorial decision: Revision requested 20 Jan, 2025 Reviewers invited by journal 20 Jan, 2025 Editor assigned by journal 15 Jan, 2025 Submission checks completed at journal 15 Jan, 2025 First submitted to journal 14 Jan, 2025 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-5827543","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":402526665,"identity":"5c41f212-b54a-4546-8019-f592a23cd7e2","order_by":0,"name":"Trong Thanh Nguyen","email":"","orcid":"","institution":"Vietnam National University, Hanoi","correspondingAuthor":false,"prefix":"","firstName":"Trong","middleName":"Thanh","lastName":"Nguyen","suffix":""},{"id":402526666,"identity":"8e1bb5a3-18eb-43a0-98fc-2160dc9ea626","order_by":1,"name":"Le Cuong Nguyen","email":"","orcid":"","institution":"Electric Power University","correspondingAuthor":false,"prefix":"","firstName":"Le","middleName":"Cuong","lastName":"Nguyen","suffix":""},{"id":402526667,"identity":"9cd663a2-59ec-4bc2-a927-62516c6999b1","order_by":2,"name":"Thi-Thanh-Tan Nguyen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYPACGwjFQ4xasKIDCWmkazlMghZ79rPHHn/8cV7OXCKB8cHbNobE7QRt4clLNziQcNvYckYCs+FcoJadDQQdlmMmAdSSuOFGAps0bxuDscEBQlr434C0nKsHamH/TZwWCbAtBxIMgLYwA7XIEdZyA2jLmbRkw509D5sl55yTIKyFvR9oS4WNnbw5e/LBD2/KbHgIaoEDAwbGBiAlQax6sJZRMApGwSgYBTgAAAdMO+sskt1yAAAAAElFTkSuQmCC","orcid":"","institution":"Electric Power University","correspondingAuthor":true,"prefix":"","firstName":"Thi-Thanh-Tan","middleName":"","lastName":"Nguyen","suffix":""}],"badges":[],"createdAt":"2025-01-14 13:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5827543/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5827543/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11760-025-04273-7","type":"published","date":"2025-06-05T15:57:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84243132,"identity":"9d8bde51-e486-4a2e-96c6-49359a4ad985","added_by":"auto","created_at":"2025-06-09 16:12:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":877640,"visible":true,"origin":"","legend":"","description":"","filename":"DroneDetectionandRecognition.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5827543/v1_covered_1cafe815-d4d2-4798-9f21-ba1429e9e607.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Effective RF-Based Solution for Drone Detection and Recognition Amid Noise, Bluetooth, and Wi-Fi Interference","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":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Drone, Radio Frequency, Signal transformation, Spectrogram, Images, Detection, Classification, Raw IQ data, ROI, Training, Testing, Confusion matrix, Accuracy, Precision, Recall, F1-Score","lastPublishedDoi":"10.21203/rs.3.rs-5827543/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5827543/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecent advancements in drone technology have raised significant security concerns, making the classification of drone types for early warning systems increasingly vital. 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