Vehicular Outdoor Localization Using CellularNetwork Signals and Machine Learning

Vehicular Outdoor Localization Using CellularNetwork Signals and Machine Learning | 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 Vehicular Outdoor Localization Using CellularNetwork Signals and Machine Learning Abdelrahman Basha, Soumaya Yacout This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8688925/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 Accurate vehicular localization is an essential parameter for enabling intelligent fleet management and predictive maintenance, especially in connected Electric Vehicles (EVs). Global Navigation Satellite Systems, such as the Global Positioning System (GPS), often suffer from signal blockage, high energy use, and hardware constraints in dense urban areas. This paper proposed an advanced cellular network-based localization approach to support EV health monitoring by addressing two key challenges. The work addressed two linked challenges. The first challenge concerned incomplete Long Term Evolution (LTE) signal measurements along the vehicle trajectories. These data gaps occurred due to handovers between cell towers, signal obstructions, and vehicle motion. The second challenge involved the regression of these cellular features to geographic coordinates with meter-level error. This study evaluates imputation methods for missing values and introduces two methods tailored to time series of cellular signals, named Blockwise Endpoint-Averaging (BEA) and Blockwise Endpoint-Propagation (BEP). It then applied feature selection methods. These methods reduces the feature space from nineteen to seven cellular and geometric features. Several regression models then mapped the selected features to latitude and longitude, with a stacking ensemble that combines the base models with the highest validation scores. Experiments on an urban drive dataset showed that BEA imputation, feature selection, and stacking reduced the mean localization error from about 46.87 metres in the baseline to about 2.69 metres. Outdoor Localization Machine learning models LTE 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. 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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-8688925","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589013189,"identity":"fad1fbc9-0ca5-4784-b947-838c337935aa","order_by":0,"name":"Abdelrahman Basha","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Abdelrahman","middleName":"","lastName":"Basha","suffix":""},{"id":589013190,"identity":"3e063df5-10d1-4cd8-8c52-90acb0f76fee","order_by":1,"name":"Soumaya Yacout","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Soumaya","middleName":"","lastName":"Yacout","suffix":""}],"badges":[],"createdAt":"2026-01-24 19:53:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8688925/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8688925/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103505076,"identity":"58230434-a8ec-440c-8a33-a5ab76369452","added_by":"auto","created_at":"2026-02-26 13:23:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":856410,"visible":true,"origin":"","legend":"","description":"","filename":"1stPaperPowerConsumptionCellularNetworkBasedOutdoorLocalization.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8688925/v1_covered_4f009210-324b-478d-81c8-2fb50b6c7519.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Vehicular Outdoor Localization Using CellularNetwork Signals and Machine Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Outdoor Localization, Machine learning models, LTE","lastPublishedDoi":"10.21203/rs.3.rs-8688925/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8688925/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurate vehicular localization is an essential parameter for enabling intelligent fleet management and predictive maintenance, especially in connected Electric Vehicles (EVs). 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