Machine Learning Algorithms for FCB Estimation in PPP-AR Technique

Machine Learning Algorithms for FCB Estimation in PPP-AR Technique | 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 Machine Learning Algorithms for FCB Estimation in PPP-AR Technique Furkan Karlitepe, Bahattin Erdogan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3953531/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 Precise Point Positioning (PPP) technique have shown continuous improvement regarding positioning. The recent developments in PPP-AR (Ambiguity Resolution) have facilitated the resolution of integer ambiguity. Thereby, the observation period needed for the convergence time, which is considered as a disadvantage of PPP, has been shortened. Furthermore, the integer ambiguity is not independent source of error but influenced by the Fractional Cycle Bias (FCB) products. This study aims to investigate the effects of the FCBs in PPP-AR technique, which reduces the convergence time. For FCB estimation, machine learning was applied by modifying the functional model of the Single Difference Between Satellite (SDBS) technique. The incorporation of these algorithms enables estimation of FCB values, even for relatively small values. It can be asserted that the support vector machine performs than both the random forest and the SDBS model regarding success. For the integer ambiguity solution PPP-AR demonstrates superior performance compared to PPP. PPP PPP-AR Integer Ambiguity FCB estimation Machine Learning Algorithms Full Text Additional Declarations Competing interest reported. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding: This study is not funded by any institution or organization 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-3953531","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273143609,"identity":"ff9d28e5-b508-46ec-b807-dc18a1b93d8f","order_by":0,"name":"Furkan Karlitepe","email":"data:image/png;base64,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","orcid":"","institution":"Gaziosmanpaşa University","correspondingAuthor":true,"prefix":"","firstName":"Furkan","middleName":"","lastName":"Karlitepe","suffix":""},{"id":273143610,"identity":"14f0d450-564f-4bee-a8b7-95268d65b0e6","order_by":1,"name":"Bahattin Erdogan","email":"","orcid":"","institution":"Yıldız Technical University","correspondingAuthor":false,"prefix":"","firstName":"Bahattin","middleName":"","lastName":"Erdogan","suffix":""}],"badges":[],"createdAt":"2024-02-13 12:47:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3953531/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3953531/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51401002,"identity":"ac37ecce-48a7-4414-9014-eb11699d978d","added_by":"auto","created_at":"2024-02-20 23:24:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":500029,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3953531/v1_covered_454f066f-8363-4f5f-accb-df310b7faf8f.pdf"}],"financialInterests":"Competing interest reported. 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