Sensor Fusion for Drone Position and Attitude Estimation using Extended Kalman Filter

Sensor Fusion for Drone Position and Attitude Estimation using Extended Kalman Filter | 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 Sensor Fusion for Drone Position and Attitude Estimation using Extended Kalman Filter Md Samiul Haque Motayed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7087667/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 real-time estimation of a drone’s position, velocity, and orientation is crucial for stable navigation, path tracking, and autonomous control. However, achieving robust state estimation is challenging due to sensor noise, environmental disturbances, and the nonlinear nature of drone dynamics. This project investigates the use of an Extended Kalman Filter (EKF) to fuse data from multiple sensors—specifically, GPS, an Inertial Measurement Unit (IMU), and a barometric altimeter—to estimate the full 9-state vector of a drone in various motion scenarios. A high-fidelity simulation environment is developed in MATLAB Simulink, incorporating a physics-based six degrees-of-freedom (6DOF) drone model. Sensor measurements are generated by simulating realistic sampling rates and noise characteristics for each sensor type. The EKF is implemented to fuse GPS-provided position and velocity, IMU-derived acceleration and angular velocity, and barometric altitude into a unified state estimate. The effectiveness of the filter is validated using different trajectory profiles—including linear, sinusoidal, and linearly increasing motions. Evaluation metrics such as Root Mean Square Error (RMSE) are used to quantify estimation performance. The results confirm that the EKF significantly reduces sensor noise and drift, resulting in reliable full-state estimation even in complex dynamic conditions. Mechanical Engineering Drone Estimation Measurement Extended Kalman Filter Sensor Full Text Additional Declarations The authors declare no competing interests. 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-7087667","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483194750,"identity":"6192bbe2-07d3-4025-a14d-f740ddc10efe","order_by":0,"name":"Md Samiul Haque Motayed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYJACg4QKGzkUAYJaCh6cSTNGVk1Yy8eHLYcTG4jWojsjO3FDYgNz+tr2M8YfGNv+yDOwN2+TwKfF7MzZzQaJO9hyt53JMZNgbDMwbOA5VoZfy/HebQaJZ3hytx1IS2MAamFskADqxavlMO/2H4ltEulm558lAx1mYN8g/4aAluO9GwwS2wwSzG4kHwA5LLFBgoeAljNnNxgknEkw3Hbj8TGJhHPGyW08acUWeLXcyN1g+KPiv7zZ+cTmDx/K5Gz72Q9vvIFPCypIAGI24pWPglEwCkbBKMAFAK7sTplEQ7C9AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0005-3137-7091","institution":"The University of Texas at Arlington","correspondingAuthor":true,"prefix":"","firstName":"Md","middleName":"Samiul Haque","lastName":"Motayed","suffix":""}],"badges":[],"createdAt":"2025-07-10 00:00:34","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7087667/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7087667/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86462361,"identity":"f3692324-1a6c-4156-8f08-156ba9c8f2ac","added_by":"auto","created_at":"2025-07-11 02:18:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1317458,"visible":true,"origin":"","legend":"","description":"","filename":"SensorFusionforDronePositionandAttitudeEstimationusingExtendedKalmanFilter.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7087667/v1_covered_5f1ae300-a140-4990-b240-011893602182.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSensor Fusion for Drone Position and Attitude Estimation using Extended Kalman Filter\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"The University of Texas at Arlington","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":"Drone, Estimation, Measurement, Extended Kalman Filter, Sensor","lastPublishedDoi":"10.21203/rs.3.rs-7087667/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7087667/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate real-time estimation of a drone’s position, velocity, and orientation is crucial for stable navigation, path tracking, and autonomous control. 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