Calibrating resolver for LEO satellite ground stations utilizing Deep Neural Network

Calibrating resolver for LEO satellite ground stations utilizing Deep Neural Network | 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 Calibrating resolver for LEO satellite ground stations utilizing Deep Neural Network Yasin Sancar, Ramiz Görkem Birdal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4720090/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 Low Earth Orbit (LEO) ground stations rely on precise rotational position feedback systems, such as resolvers, to accurately track and communicate with satellites. Nevertheless, resolvers often suffer from calibration errors due to mechanical misalignments, temperature variations, and other environmental factors. This study proposes a method for calibrating 16-bit resolvers using Deep Neural Networks (DNNs) to enhance accuracy and reliability in LEO ground station applications. In traditional calibrations, known errors are compensated for by mechanically aligning the resolver. This process often requires manual intervention and periodic recalibration to maintain accuracy. Instead, proposed automated calibration reduces the need for frequent manual calibration, which saves time and resources. Error profiles and characteristics of resolver were easily revealed by DNN's multiple layers of neurons, capable of learning complex input-output mappings. As a result of this software-based error compensation method, target distortion ratio of 16-bit resolver was improved from ~ ±%10 to ~ ±%2. This means the error in the target angle, which can be as high as 1°, can be decreased to a level of 0.2° by using the DNN for calibration. It is also possible to overcome nonideal characteristics of a resolver such as amplitude imbalance, quadrature error, and inductive harmonics with the method presented in this study. Resolver LEO ground stations DNN 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-4720090","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328998552,"identity":"1f0d3462-6598-41bf-bddb-61ce9d76e4a9","order_by":0,"name":"Yasin Sancar","email":"","orcid":"","institution":"Ataturk University","correspondingAuthor":false,"prefix":"","firstName":"Yasin","middleName":"","lastName":"Sancar","suffix":""},{"id":328998556,"identity":"c97d450b-7a56-4d06-9868-3a2853b57272","order_by":1,"name":"Ramiz Görkem Birdal","email":"data:image/png;base64,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","orcid":"","institution":"Istanbul University - Cerrahpasa","correspondingAuthor":true,"prefix":"","firstName":"Ramiz","middleName":"Görkem","lastName":"Birdal","suffix":""}],"badges":[],"createdAt":"2024-07-10 19:01:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4720090/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4720090/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62312736,"identity":"4abac2c8-0426-4e3d-8723-276b4b81ce04","added_by":"auto","created_at":"2024-08-12 21:38:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":536614,"visible":true,"origin":"","legend":"","description":"","filename":"CalibratingresolverforLEOsatellitegroundstationsutilizingDeepNeuralNetwork.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4720090/v1_covered_d199b7ea-99d0-499b-bd02-660144463f1c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Calibrating resolver for LEO satellite ground stations utilizing Deep Neural Network","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":"Resolver, LEO ground stations, DNN","lastPublishedDoi":"10.21203/rs.3.rs-4720090/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4720090/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLow Earth Orbit (LEO) ground stations rely on precise rotational position feedback systems, such as resolvers, to accurately track and communicate with satellites. Nevertheless, resolvers often suffer from calibration errors due to mechanical misalignments, temperature variations, and other environmental factors. This study proposes a method for calibrating 16-bit resolvers using Deep Neural Networks (DNNs) to enhance accuracy and reliability in LEO ground station applications. In traditional calibrations, known errors are compensated for by mechanically aligning the resolver. This process often requires manual intervention and periodic recalibration to maintain accuracy. Instead, proposed automated calibration reduces the need for frequent manual calibration, which saves time and resources. Error profiles and characteristics of resolver were easily revealed by DNN's multiple layers of neurons, capable of learning complex input-output mappings. As a result of this software-based error compensation method, target distortion ratio of 16-bit resolver was improved from ~ \u0026plusmn;%10 to ~ \u0026plusmn;%2. This means the error in the target angle, which can be as high as 1\u0026deg;, can be decreased to a level of 0.2\u0026deg; by using the DNN for calibration. It is also possible to overcome nonideal characteristics of a resolver such as amplitude imbalance, quadrature error, and inductive harmonics with the method presented in this study.\u003c/p\u003e","manuscriptTitle":"Calibrating resolver for LEO satellite ground stations utilizing Deep Neural Network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-05 19:13:52","doi":"10.21203/rs.3.rs-4720090/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"ac2c99ff-d85f-44c5-9827-56a11d96517f","owner":[],"postedDate":"August 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-12T21:30:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-05 19:13:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4720090","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4720090","identity":"rs-4720090","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}