Effect of Noise Correlation Coefficient on Joint Recursive Least Squares Parameters and State Estimation of Linear Stochastic State-Space System

Effect of Noise Correlation Coefficient on Joint Recursive Least Squares Parameters and State Estimation of Linear Stochastic State-Space System | 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 Effect of Noise Correlation Coefficient on Joint Recursive Least Squares Parameters and State Estimation of Linear Stochastic State-Space System Khalid Abd El Mageed Hag Elamin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1566070/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 The aim of this article is to investigate how to estimate parameters and states jointly for the linear stochastic system with deterministic control inputs. The cross-correlation between process noise and measurement noise in Kalman filtering re-formation cycles is utilized to derive a Kalman filtering with correlated noises based recursive generalized extended least squares (KF-CN-RGELS) algorithm for jointly estimating parameters and system states. The performance analysis of different correlation coefficients between process and measurement noises shows that the accuracy of the identified parameters and states is proportional to the positive correlation coefficients. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithms. Correlated noises least squares linear stochastic system parameter estimation Full Text Declaration 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. 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-1566070","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":101860669,"identity":"82dd2c60-0830-4e23-b9d6-76216b461b22","order_by":0,"name":"Khalid Abd El Mageed Hag Elamin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIie3PMUvEMBTA8YTAdal0fYdfolMQLO0HcUkI1OUUQZAbbshRaL+FfgLhXA7HQkCXYtcet9jB/VwEF/E1cJM06iaYP2R48H48QojP9xcL8AkiBAmonu7mCY50WTsJ2xNG9SE0+UD094RYQggclMaOThIx9rLq7/PzqGDLDiZtelsZvLJITsbItJjwTjazSzC0OLoIt2rdSCQP+ZkeIbEhSMq51IaWALBVvEZCtRklmQneLLkZSBg/Kd72bhKzcLgykytLRJ3i6CZgwiskubzDvxxDrQTv8Ipw/CWqqvXmvVTy+rHqN68facbb0/55t0hGyZek3RQ/XR/KfrPs8/l8/6NPqH1n4Q18l40AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-7791-3591","institution":"Buraydah Private Colleges: Buraydah Colleges(Former)","correspondingAuthor":true,"prefix":"","firstName":"Khalid","middleName":"Abd El Mageed Hag","lastName":"Elamin","suffix":""}],"badges":[],"createdAt":"2022-04-17 10:56:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1566070/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1566070/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":21078530,"identity":"2528d090-38af-4720-a55e-09cf587bde90","added_by":"auto","created_at":"2022-05-04 17:29:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":571498,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1566070/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"Effect of Noise Correlation Coefficient on Joint Recursive Least Squares Parameters and State Estimation of Linear Stochastic State-Space System","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1566070/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."},{"header":"Declaration","content":"\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"}],"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":true,"isPdf":false,"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":"Correlated noises, least squares, linear stochastic system, parameter estimation","lastPublishedDoi":"10.21203/rs.3.rs-1566070/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1566070/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The aim of this article is to investigate how to estimate parameters and states jointly for the linear stochastic system with deterministic control inputs. The cross-correlation between process noise and measurement noise in Kalman filtering re-formation cycles is utilized to derive a Kalman filtering with correlated noises based recursive generalized extended least squares (KF-CN-RGELS) algorithm for jointly estimating parameters and system states. The performance analysis of different correlation coefficients between process and measurement noises shows that the accuracy of the identified parameters and states is proportional to the positive correlation coefficients. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithms.","manuscriptTitle":"Effect of Noise Correlation Coefficient on Joint Recursive Least Squares Parameters and State Estimation of Linear Stochastic State-Space System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-05-04 17:29:00","doi":"10.21203/rs.3.rs-1566070/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":"d9edba49-a91b-4851-adf9-b9f0f44370cb","owner":[],"postedDate":"May 4th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-01-25T02:33:42+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-04 17:29:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1566070","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1566070","identity":"rs-1566070","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}