CSL-based Detection Method for BeiDou Spoofing Interference Signals

CSL-based Detection Method for BeiDou Spoofing Interference Signals | 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 CSL-based Detection Method for BeiDou Spoofing Interference Signals Gao Yaping, Yang Jingxin, Su Chengeng, Jiang Zhen, Han Junqiang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7489208/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Anti-jamming and anti-spoofing technologies for the BeiDou Navigation Satellite System (BDS) have become major challenges in current research. Due to the openness of civilian signals and power limitations, user terminals are highly vulnerable to various attacks, including spoofing interference. This paper proposed a CNN-SA-LSTM (CSL) model for BDS spoofing interference detection method based on a CNN-LSTM combined model, leveraging discriminative features derived from signal reception characteristics. The model utilizes a combination of signal characteristics, including pseudorange, carrier phase, carrier-to-noise ratio, and Doppler shift, to detect BDS signal spoofing interference. A field test was conducted, and the simulation results showed that compared with single CNN, RNN, or LSTM models, the combined detection model proposed in this paper can effectively detect multiple types of interference data, with a detection accuracy rate of 93.56%, which is higher than that of single models. This shows that the method has strong pattern recognition and generalization capabilities, significantly improving detection accuracy and robustness. CSL BDS spoofing detection deep learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 10 Dec, 2025 Reviewers invited by journal 10 Dec, 2025 Editor assigned by journal 10 Dec, 2025 Submission checks completed at journal 01 Sep, 2025 First submitted to journal 29 Aug, 2025 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-7489208","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558544478,"identity":"8675d8e2-39d4-447d-a8a7-051f9bbb8f7a","order_by":0,"name":"Gao Yaping","email":"","orcid":"","institution":"Chengdu University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Gao","middleName":"","lastName":"Yaping","suffix":""},{"id":558544479,"identity":"3743079e-02e7-438a-8521-4a740c57f76c","order_by":1,"name":"Yang Jingxin","email":"","orcid":"","institution":"Chengdu University of 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Signals","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"gps-solutions","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gpss","sideBox":"Learn more about [GPS Solutions](http://link.springer.com/journal/10291)","snPcode":"10291","submissionUrl":"https://submission.nature.com/new-submission/10291/3","title":"GPS Solutions","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"CSL, BDS, spoofing detection, deep learning","lastPublishedDoi":"10.21203/rs.3.rs-7489208/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7489208/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnti-jamming and anti-spoofing technologies for the BeiDou Navigation Satellite System (BDS) have become major challenges in current research. Due to the openness of civilian signals and power limitations, user terminals are highly vulnerable to various attacks, including spoofing interference. This paper proposed a CNN-SA-LSTM (CSL) model for BDS spoofing interference detection method based on a CNN-LSTM combined model, leveraging discriminative features derived from signal reception characteristics. The model utilizes a combination of signal characteristics, including pseudorange, carrier phase, carrier-to-noise ratio, and Doppler shift, to detect BDS signal spoofing interference. A field test was conducted, and the simulation results showed that compared with single CNN, RNN, or LSTM models, the combined detection model proposed in this paper can effectively detect multiple types of interference data, with a detection accuracy rate of 93.56%, which is higher than that of single models. This shows that the method has strong pattern recognition and generalization capabilities, significantly improving detection accuracy and robustness.\u003c/p\u003e","manuscriptTitle":"CSL-based Detection Method for BeiDou Spoofing Interference Signals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 14:35:45","doi":"10.21203/rs.3.rs-7489208/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"179510822520525232727396998312969688924","date":"2025-12-11T03:21:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-11T03:19:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-11T03:18:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-02T03:06:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"GPS Solutions","date":"2025-08-29T13:42:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"gps-solutions","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gpss","sideBox":"Learn more about [GPS Solutions](http://link.springer.com/journal/10291)","snPcode":"10291","submissionUrl":"https://submission.nature.com/new-submission/10291/3","title":"GPS Solutions","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e658dece-c429-4334-b833-4d606e3c1369","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-16T14:35:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 14:35:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7489208","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7489208","identity":"rs-7489208","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}