A Parallel Hardware Architecture for Real-Time Extended KalmanFilter Acceleration in Multi-Sensor Autonomous Systems

A Parallel Hardware Architecture for Real-Time Extended KalmanFilter Acceleration in Multi-Sensor Autonomous Systems | 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 A Parallel Hardware Architecture for Real-Time Extended KalmanFilter Acceleration in Multi-Sensor Autonomous Systems SATYA PRAKASH KATTUNGA, V S A L SREEPRAD MANDA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9660548/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 Software-based Extended Kalman Filter (EKF) implementations execute floating-point matrix operations sequentially. This creates processing bottlenecks. This paper presents a hardware-accelerated EKF architecture mapped to a Field Programmable Gate Array (FPGA) to resolve these computational limits. The system allocates dedicated hardware logic blocks to independently process radar, LiDAR, and camera data streams. A host processor manages data acquisition. It translates polar coordinates to Cartesian space and transmits the localized parameters to the FPGA via serial communication. The FPGA concurrently executes the prediction algorithms, Jacobian matrix computations, and covariance updates. This structural parallelism guarantees deterministic timing. The architecture completes the full data exchange and computation cycle within a 10 to 20-millisecond window. Offloading these complex matrix computations directly increases state estimation speed for autonomous navigation tasks. Extended Kalman Filter Field Programmable Gate Arrays Multi-Sensor Fusion Hardware Acceleration Autonomous Navigation State Estimation 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. 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-9660548","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638587374,"identity":"8ab52aa1-8172-4670-9082-aa388e255ea1","order_by":0,"name":"SATYA PRAKASH KATTUNGA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYBACfvbGhgMJPBI8/OwNQG4FEDMzN+DVItlz+OCDDzI2MpI9B4DcMyAtjPi1GNxISzacYZNmY3DDgYGBsQ0kRkALw4EcM2menMM8kjOYD374OK82mr8dqOVHxTacOhgbzgC1nDnMwy/dliw5c9vx3BmHGRsYe87cxqmFmbHHTJq3B2jLnDNmzLzbjuU2ALUwM7bh1sLGzAPU8u8wj8GN/G/Mf+ccy51PSAsPGxvQ+zxpQC05bMCwqsndQEiLBA8zMJB5bHgke44ZA/GB3I1ALQfx+cX+/kNwVNrzszc//PCjpi533nlgTP2owK0FHRwGkweIVg8EdaQoHgWjYBSMghECAJTxX7Ll3qKkAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"SATYA","middleName":"PRAKASH","lastName":"KATTUNGA","suffix":""},{"id":638587378,"identity":"df80186d-c405-439a-bc78-fb8c70cc8ade","order_by":1,"name":"V S A L SREEPRAD MANDA","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"V","middleName":"S A L SREEPRAD","lastName":"MANDA","suffix":""}],"badges":[],"createdAt":"2026-05-09 06:53:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9660548/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9660548/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109068167,"identity":"ed3ae774-6b3a-4056-b6af-a9c833b38b40","added_by":"auto","created_at":"2026-05-12 10:04:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1532788,"visible":true,"origin":"","legend":"","description":"","filename":"MANUSCRIPTLATEX.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9660548/v1_covered_47dfe106-a91d-44b7-9f25-c123c6085073.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Parallel Hardware Architecture for Real-Time Extended KalmanFilter Acceleration in Multi-Sensor Autonomous Systems","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":"Extended Kalman Filter, Field Programmable Gate Arrays, Multi-Sensor Fusion, Hardware Acceleration, Autonomous Navigation, State Estimation","lastPublishedDoi":"10.21203/rs.3.rs-9660548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9660548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoftware-based Extended Kalman Filter (EKF) implementations execute floating-point matrix operations sequentially. 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