Combining Semiparametric and Machine Learning Approaches for Short-term Prediction of Satellite Clock Bias

Combining Semiparametric and Machine Learning Approaches for Short-term Prediction of Satellite Clock Bias | 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 Article Combining Semiparametric and Machine Learning Approaches for Short-term Prediction of Satellite Clock Bias xiong pan, wanzhuo zhao, lihong jin, qingsong ai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4597800/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Accurate modeling of satellite clock bias (SCB) is critical for enhancing high-precision positioning capabilities. Existing approaches, including semiparametric adjustment models and neural networks, address the nonlinearity and non-stationarity of SCB time series, as well as potential distortions from trend and noise component overlap. However, its practicality is limited when selecting the kernel functions in semiparametric models and the initial parameters in neural networks. This paper proposes a novel integrated model, the Semi-LFA-Informer (SLFAI) model, which combines semiparametric techniques with optimized self-attention neural networks to solve the above limitations. We utilize the proposed model to conduct the SCB prediction for BDS-3, and the results are compared with those of the quadratic polynomial (QP), spectral analysis (SA), and long short-term memory (LSTM) models in terms of prediction stability and accuracy. The experimental results show that the proposed method can not only effectively solve the problem of the generalization ability, but also significantly enhance the computational efficiency and accuracy. The SLFAI model achieves average prediction accuracies exceeding 0.15 ns, 0.25 ns, and 0.35 ns for 3-hour, 6-hour, and 12-hour forecasts, respectively, representing a new approach to acquiring high-quality SCB. Semiparametric Kernel Function Neural Network Satellite Clock Bias Prediction Computational Efficiency Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 30 Dec, 2024 Reviews received at journal 05 Dec, 2024 Reviewers agreed at journal 05 Dec, 2024 Reviews received at journal 13 Jul, 2024 Reviewers agreed at journal 02 Jul, 2024 Reviewers invited by journal 02 Jul, 2024 Editor assigned by journal 02 Jul, 2024 Editor invited by journal 24 Jun, 2024 Submission checks completed at journal 19 Jun, 2024 First submitted to journal 18 Jun, 2024 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. 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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-4597800","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":322617831,"identity":"5003f78c-829b-4581-9be3-073e9897778e","order_by":0,"name":"xiong pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYHCCxMMMFRJy8qRoSTjMcMbC2LCBFD2HGdsqEhkOEKvcXCLhweHCeRIJjA3MDx/dIEaLZc+BhMMzt0nksTOwGRvnEKPF4HhDwmHebRLFjA08bNLEaTkM9D7vHInEhgNEawHb0kCKFrBfeI5JGBs2E+sXc4mcxMc8NXVy8uzNDx8T5zAGngQIi5kY5RAt7AeIVTsKRsEoGAUjFQAAk3wwo5Qb9/gAAAAASUVORK5CYII=","orcid":"","institution":"School of Computer Science and Artificial Intelligence, Wuhan Textile University","correspondingAuthor":true,"prefix":"","firstName":"xiong","middleName":"","lastName":"pan","suffix":""},{"id":322617832,"identity":"7000b615-9d29-439f-a6a5-b2fd93be0875","order_by":1,"name":"wanzhuo zhao","email":"","orcid":"","institution":"School of Computer Science and Artificial Intelligence, Wuhan Textile University","correspondingAuthor":false,"prefix":"","firstName":"wanzhuo","middleName":"","lastName":"zhao","suffix":""},{"id":322617833,"identity":"4af26d58-c318-44fa-8279-fb38c03a66ed","order_by":2,"name":"lihong jin","email":"","orcid":"","institution":"School of Mathematical and Physical Sciences, Wuhan Textile University","correspondingAuthor":false,"prefix":"","firstName":"lihong","middleName":"","lastName":"jin","suffix":""},{"id":322617834,"identity":"d51e9046-2b92-42b5-834d-6d8e2889b1bc","order_by":3,"name":"qingsong ai","email":"","orcid":"","institution":"Changjiang Space Information Technology Engineering Co., ltd. 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Existing approaches, including semiparametric adjustment models and neural networks, address the nonlinearity and non-stationarity of SCB time series, as well as potential distortions from trend and noise component overlap. However, its practicality is limited when selecting the kernel functions in semiparametric models and the initial parameters in neural networks. This paper proposes a novel integrated model, the Semi-LFA-Informer (SLFAI) model, which combines semiparametric techniques with optimized self-attention neural networks to solve the above limitations. We utilize the proposed model to conduct the SCB prediction for BDS-3, and the results are compared with those of the quadratic polynomial (QP), spectral analysis (SA), and long short-term memory (LSTM) models in terms of prediction stability and accuracy. 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The SLFAI model achieves average prediction accuracies exceeding 0.15 ns, 0.25 ns, and 0.35 ns for 3-hour, 6-hour, and 12-hour forecasts, respectively, representing a new approach to acquiring high-quality SCB.\u003c/p\u003e","manuscriptTitle":"Combining Semiparametric and Machine Learning Approaches for Short-term Prediction of Satellite Clock Bias","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-05 09:36:30","doi":"10.21203/rs.3.rs-4597800/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-30T13:25:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-05T22:16:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115829745365231290051792212742419418862","date":"2024-12-05T13:48:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-13T05:24:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111823506134146696130926370492485174148","date":"2024-07-02T14:08:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-02T09:00:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-02T08:57:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-24T09:59:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-19T09:43:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-18T06:33:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"632c1f44-b82a-4a7f-85f2-62df41eac876","owner":[],"postedDate":"July 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-14T16:18:11+00:00","versionOfRecord":{"articleIdentity":"rs-4597800","link":"https://doi.org/10.1038/s41598-025-95876-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-07 16:05:30","publishedOnDateReadable":"April 7th, 2025"},"versionCreatedAt":"2024-07-05 09:36:30","video":"","vorDoi":"10.1038/s41598-025-95876-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-95876-0","workflowStages":[]},"version":"v1","identity":"rs-4597800","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4597800","identity":"rs-4597800","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}