Cognitive MobileNetV2-Based Micro-Doppler Analysis for Small UAV Detection and Classification

Cognitive MobileNetV2-Based Micro-Doppler Analysis for Small UAV Detection and Classification | 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 Cognitive MobileNetV2-Based Micro-Doppler Analysis for Small UAV Detection and Classification Sujata Patil, Madan Mali, Supriya Rajankar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8863488/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 The growing use of small unmanned aerial vehicles (UAVs) in civilian and defense airspace are increased the need for reliable detection method which can operate under clutter and low altitude condition. Radar based sensing using micro-Doppler signatures provide valuable motion related information however, effective use of these signatures still remain challenging because of background interference and limited signal strength. In this study, a lightweight deep learning framework based on MobileNetV2 is investigate for detection and classification of small UAV using micro-Doppler spectrogram derived from DIAT-uSAT dataset. Raw radar signal is processed to obtain time frequency representation which then analyzed using multiple convolution neural network architecture to evaluate the classification performance. Comparative experiment involving EfficientNetB0, DenseNet121, Xception, ResNet50 and VGG16 shows that MobileNetV2 achieve faster convergence and more stable generalization on compact spectrogram representation. The proposed model achieved training accuracy of 96.1% and validation accuracy of 95% with minimum validation loss. These result suggest that properly designed lightweight convolution network can effectively capture discriminative micro Doppler pattern and suitable for real time UAV surveillance application in complex environment. Cognitive MobileNetV2 Small UAV Detection Micro-Doppler Signatures Radar Spectrograms Deep Learning Lightweight Convolutional Neural 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. 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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-8863488","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601718375,"identity":"90a97689-37fd-4e07-b845-12b9e743f765","order_by":0,"name":"Sujata Patil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABQ0lEQVRIie2RT0uEQBiHXxHsYux1IMivMEsQQd37GjMIdhEzhGWDDoagl8irN7+C+w1cBLvYnoXtoJc9ddglWDxY5Ax2qBG6Bvlc3vn38HuHF2Bk5G9C+irdp/1GqgDzFQZQhpWUFUVyvxQZ/6JAr/DKFQX14YOKFpH6bXsH1iT2upTWsCaBl9829oulXWYJbGfZTwWXREdpDg7Ku8Yk33RQkRtrFW+caWHYUrQSFUQIShWgLlfcOU2QeboGnNGFa2L50BeUrjG9ST+AxkyBlinX+5uGKeErlt9FBUpioKUPNOGKYrIUBdROiVGXIokKLirj7PkROdOcsr8YNCrykyOmJGhjLx9WV0JjgamX8/2FdZw91RW0Og0Dr941bZcS6ouqmZ2LQ1HZABCfh9x+ayDtJyZwwI+JeKG5g+9HRkZG/h+fP1yCi8RJrWsAAAAASUVORK5CYII=","orcid":"","institution":"Savitribai Phule Pune University","correspondingAuthor":true,"prefix":"","firstName":"Sujata","middleName":"","lastName":"Patil","suffix":""},{"id":601718376,"identity":"fde84430-9180-4898-b46b-3024d29ad045","order_by":1,"name":"Madan Mali","email":"","orcid":"","institution":"Savitribai Phule Pune University","correspondingAuthor":false,"prefix":"","firstName":"Madan","middleName":"","lastName":"Mali","suffix":""},{"id":601718377,"identity":"f92fb8f3-c877-4620-bc50-bff1bdb20587","order_by":2,"name":"Supriya Rajankar","email":"","orcid":"","institution":"Savitribai Phule Pune University","correspondingAuthor":false,"prefix":"","firstName":"Supriya","middleName":"","lastName":"Rajankar","suffix":""}],"badges":[],"createdAt":"2026-02-12 15:08:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8863488/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8863488/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107909948,"identity":"6d450444-b65b-40ca-8af9-ebf3a70f1c2b","added_by":"auto","created_at":"2026-04-27 13:12:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":598540,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptwordf1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8863488/v1_covered_09c5f023-d12e-44d3-bc6c-01c96e55db16.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive MobileNetV2-Based Micro-Doppler Analysis for Small UAV Detection and Classification","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":"Cognitive MobileNetV2, Small UAV Detection, Micro-Doppler Signatures, Radar Spectrograms, Deep Learning, Lightweight Convolutional Neural","lastPublishedDoi":"10.21203/rs.3.rs-8863488/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8863488/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe growing use of small unmanned aerial vehicles (UAVs) in civilian and defense airspace are increased the need for reliable detection method which can operate under clutter and low altitude condition. 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