Comparative Analysis of Statistical Filtering and Deep Learning for GPS Trajectory Denoising in Personal Exposure Assessment

Comparative Analysis of Statistical Filtering and Deep Learning for GPS Trajectory Denoising in Personal Exposure Assessment | 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 Comparative Analysis of Statistical Filtering and Deep Learning for GPS Trajectory Denoising in Personal Exposure Assessment Moobeom Hong, Hyunwoo Jeon, Kiyoung Lee, Sungho Won This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9148926/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 Background Accurate assessment of personal air pollution exposure is essential in environmental epidemiological studies. Indirect approaches often estimate exposures using measurements from the nearest monitoring station based on an individual’s location. Although these locations are typically extracted using devices equipped with global positioning systems (GPS), raw GPS signals frequently lack indicators of positional accuracy. This deficiency makes it difficult to reliably identify positional errors within continuous GPS tracking data across diverse time–activity patterns. Objective This study aims to develop and systematically evaluate GPS correction models designed to restore accurate personal movement trajectories Methods We used a dataset from the Korean Air pollutant EXposure (KAPEX) model project, which includes time-location diaries labeling participants’ locations at one-minute intervals and positional coordinates simultaneously tracked with two GPS devices. A GPS trajectory from one device providing only raw signals were used to correction target and verified with the other reference trajectory, preprocessed with GPS signal quality information. The correction models were developed employing both statistical state-space and deep-learning based algorithms. Results Kalman Filter consistently demonstrated robust GPS correction performance in terms of denoising accuracy, computational efficiency, and trajectory smoothness when compared with deep learning–based models. Conversely, deep learning approaches exhibited reasonable denoising capability primarily in indoor settings characterized by frequent GPS signal degradation. Personal exposure assessment Global positioning system Denoising Kalman Filter Deep learning Full Text Additional Declarations No competing interests reported. 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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-9148926","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607885783,"identity":"89923863-aef5-464b-8e45-dbf4e66751d6","order_by":0,"name":"Moobeom Hong","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Moobeom","middleName":"","lastName":"Hong","suffix":""},{"id":607885786,"identity":"92df35be-9d38-495c-8b52-e2319de12bec","order_by":1,"name":"Hyunwoo Jeon","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Hyunwoo","middleName":"","lastName":"Jeon","suffix":""},{"id":607885788,"identity":"e7bfc75a-e4e9-465e-8f31-f942a54d8dfd","order_by":2,"name":"Kiyoung Lee","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Kiyoung","middleName":"","lastName":"Lee","suffix":""},{"id":607885790,"identity":"2568a81a-ce47-46f0-80b5-eafe0ac7a86d","order_by":3,"name":"Sungho Won","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAo0lEQVRIiWNgGAWjYJCCAx+gDMYGInUwHpxBqhbmwzwkaTE43nvgsM2fO4kN7IcfMM7cQ4yWM+cSDue2PUts4EkzYNzwjAgtZjdyDA7nNhxObGDIYWB8cIBYLRZ/gFr435CihYENqEUCaMsGYrTYnzljcLC37bBxm8Qzg4MziNEi2d5j/OHHn8Oy/fzJDx/2EKMFDtiAmCQNo2AUjIJRMArwAAAUVz3KJtw6TwAAAABJRU5ErkJggg==","orcid":"","institution":"Seoul National University","correspondingAuthor":true,"prefix":"","firstName":"Sungho","middleName":"","lastName":"Won","suffix":""}],"badges":[],"createdAt":"2026-03-17 12:51:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9148926/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9148926/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105330546,"identity":"e75e5f65-1ee3-4363-8e0d-343a05dfdede","added_by":"auto","created_at":"2026-03-24 20:39:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1039929,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptJHIR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9148926/v1_covered_12eedd5c-cdb9-4d80-84d0-1fcb35987848.pdf"},{"id":105058944,"identity":"bcb24326-4691-4a8d-a99a-e627cf9ae900","added_by":"auto","created_at":"2026-03-20 12:12:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1909102,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryJHIR.docx","url":"https://assets-eu.researchsquare.com/files/rs-9148926/v1/e1b0e2682512509253079097.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Analysis of Statistical Filtering and Deep Learning for GPS Trajectory Denoising in Personal Exposure Assessment","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":"Personal exposure assessment, Global positioning system, Denoising, Kalman Filter, Deep learning","lastPublishedDoi":"10.21203/rs.3.rs-9148926/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9148926/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAccurate assessment of personal air pollution exposure is essential in environmental epidemiological studies. Indirect approaches often estimate exposures using measurements from the nearest monitoring station based on an individual\u0026rsquo;s location. Although these locations are typically extracted using devices equipped with global positioning systems (GPS), raw GPS signals frequently lack indicators of positional accuracy. This deficiency makes it difficult to reliably identify positional errors within continuous GPS tracking data across diverse time\u0026ndash;activity patterns.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aims to develop and systematically evaluate GPS correction models designed to restore accurate personal movement trajectories\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used a dataset from the Korean Air pollutant EXposure (KAPEX) model project, which includes time-location diaries labeling participants\u0026rsquo; locations at one-minute intervals and positional coordinates simultaneously tracked with two GPS devices. A GPS trajectory from one device providing only raw signals were used to correction target and verified with the other reference trajectory, preprocessed with GPS signal quality information. The correction models were developed employing both statistical state-space and deep-learning based algorithms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eKalman Filter consistently demonstrated robust GPS correction performance in terms of denoising accuracy, computational efficiency, and trajectory smoothness when compared with deep learning\u0026ndash;based models. Conversely, deep learning approaches exhibited reasonable denoising capability primarily in indoor settings characterized by frequent GPS signal degradation.\u003c/p\u003e","manuscriptTitle":"Comparative Analysis of Statistical Filtering and Deep Learning for GPS Trajectory Denoising in Personal Exposure Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-20 12:11:38","doi":"10.21203/rs.3.rs-9148926/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":"25644793-cca4-4640-9bc0-dc28a4e04328","owner":[],"postedDate":"March 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T23:23:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-20 12:11:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9148926","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9148926","identity":"rs-9148926","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}