Uncertainty Estimation Strategy for Grouped Target Tracking under Sparse Observation

Uncertainty Estimation Strategy for Grouped Target Tracking under Sparse Observation | 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 Uncertainty Estimation Strategy for Grouped Target Tracking under Sparse Observation WanTong Chen, Zerui Cao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6872673/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 Uncontrolled reentry debris poses significant challenges to trajectory prediction due to its nonlinear dynamics, sparse observations, and environmental variability. This study proposes a hierarchical uncertainty estimation framework that integrates differential propagation, statistical modeling, and multi-source data fusion. By introducing the Atmospheric Centrifugation Effect and classifying debris into Pioneering Detection Layer (PDL), Forefront Propagation Layer (FPL), and Rear Propagation Layer (RPL) based on the area-to-mass ratio (A/M), the model captures stratified propagation patterns. Odd- and even-order nonlinearities are explicitly decomposed to characterize asymmetric drift and uncertainty expansion. Edgeworth and Gram-Charlier A series are employed to model heavy-tailed distributions, while kernel-based convolution enhances sparse target extraction. Simulation results using NASA’s breakup model and environmental data from multiple launch sites demonstrate the effectiveness of the proposed framework in improving trajectory correction and hazard zone prediction. This research provides theoretical and practical support for space debris risk management under limited observation conditions. Unpowered reentry sparse observations uncertainty estimation higher-order statistics 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-6872673","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472855101,"identity":"46404fda-2b1b-42f4-bc79-f8d9e4d387f1","order_by":0,"name":"WanTong Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYDACCQYGZiAlx8befIA0LcZ8PMcSSNOSOE8iR4E4Hfyzm489LtxxOL2NIYeB4UfFNiIsuXMs3XjmmcO5bQxnDzD2nLlNWIuBRI6ZNG8bUAtjXwIzYxtRWvK/gbSkszHzGBCrJYcNpCWBjY1YLRI30sykZ7alG7bxsCUcJMov/DOSn0kXtlnLy89/fPDBjwoitKCAAySqHwWjYBSMglGACwAAud42Q4zyoF8AAAAASUVORK5CYII=","orcid":"","institution":"Civil Aviation University of China","correspondingAuthor":true,"prefix":"","firstName":"WanTong","middleName":"","lastName":"Chen","suffix":""},{"id":472855102,"identity":"41c00b42-8a86-4845-bf8f-48ca92a6c053","order_by":1,"name":"Zerui Cao","email":"","orcid":"","institution":"Civil Aviation University of China","correspondingAuthor":false,"prefix":"","firstName":"Zerui","middleName":"","lastName":"Cao","suffix":""}],"badges":[],"createdAt":"2025-06-11 14:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6872673/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6872673/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97249287,"identity":"6846089c-7ac3-4221-b862-39a8f97d5323","added_by":"auto","created_at":"2025-12-02 13:12:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3576123,"visible":true,"origin":"","legend":"","description":"","filename":"UncertaintyEstimationStrategyforGroupedTargetTrackingunderSparseObservationtnote.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6872673/v1_covered_4d101303-53b8-470a-83d6-621596210a0d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Uncertainty Estimation Strategy for Grouped Target Tracking under Sparse Observation","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":"Unpowered reentry, sparse observations, uncertainty estimation, higher-order statistics","lastPublishedDoi":"10.21203/rs.3.rs-6872673/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6872673/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUncontrolled reentry debris poses significant challenges to trajectory prediction due to its nonlinear dynamics, sparse observations, and environmental variability. 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