End-to-End Deep Learning for Flight Trajectory Reconstruction from Multi-Station ADS-B Measurements

End-to-End Deep Learning for Flight Trajectory Reconstruction from Multi-Station ADS-B Measurements | 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 End-to-End Deep Learning for Flight Trajectory Reconstruction from Multi-Station ADS-B Measurements Yingjie Zhang, Chenxu Yang, Yingxi Ding, Bolin Lian, Xiangxiru Xiong, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8880043/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract In the field of aviation safety, ADS-B is widely adopted as an active broadcast-based aviation surveillance system, enabling aircraft to broadcast their real-time position information via onboard devices. However, the open communication protocol relied upon by ADS-B has led to increasingly frequent GPS spoofing and network hijacking attacks, posing significant threats to communication security. A reliable secondary verification method is urgently needed to revalidate the position information broadcast by aircraft. To address this issue, this paper proposes a deep learning framework that does not rely on specific message content but directly calculates aircraft trajectories through tamper-proof electromagnetic signals. Based on real flight trajectories and distributed sensor signals collected from the OpenSky dataset, we trained an End-to-End neural network, innovatively introducing heterogeneous sensor encoders and trajectory decoders, and demonstrated the effectiveness of the proposed model through empirical experiments. In comparison experiments, our proposed method achieved a breakthrough of 15.44% higher than the baseline in MDE and a single coordinate axis accuracy improvement of up to 22.77% in MRE. Finally, through ablation experiments and visualization analysis, we demonstrate the necessity of each component of the model and the overall effectiveness of trajectory reconstruction. Physical sciences/Engineering Physical sciences/Mathematics and computing ADS-B Aircraft Trajectory Reconstruction Deep learning Encoder-Decoder Architecture Neural Network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 19 Mar, 2026 Reviews received at journal 08 Mar, 2026 Reviewers agreed at journal 01 Mar, 2026 Reviews received at journal 01 Mar, 2026 Reviewers agreed at journal 28 Feb, 2026 Reviewers agreed at journal 28 Feb, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers invited by journal 24 Feb, 2026 Editor invited by journal 23 Feb, 2026 Editor assigned by journal 18 Feb, 2026 Submission checks completed at journal 18 Feb, 2026 First submitted to journal 14 Feb, 2026 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-8880043","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":596569228,"identity":"73f9f84a-83fc-443c-bd0b-70250ad2d0cc","order_by":0,"name":"Yingjie Zhang","email":"","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yingjie","middleName":"","lastName":"Zhang","suffix":""},{"id":596569229,"identity":"8520ec06-dfa7-47ee-a36d-11ef3fabdae6","order_by":1,"name":"Chenxu Yang","email":"","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Chenxu","middleName":"","lastName":"Yang","suffix":""},{"id":596569230,"identity":"d1f8cff0-5024-4534-939a-c8387045da46","order_by":2,"name":"Yingxi Ding","email":"","orcid":"","institution":"Xidian University","correspondingAuthor":false,"prefix":"","firstName":"Yingxi","middleName":"","lastName":"Ding","suffix":""},{"id":596569233,"identity":"11e7ad6f-682d-4bb8-8281-a3e4e37c778f","order_by":3,"name":"Bolin Lian","email":"","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Bolin","middleName":"","lastName":"Lian","suffix":""},{"id":596569235,"identity":"3c21bda1-943e-442b-b9f7-4230d9bcd406","order_by":4,"name":"Xiangxiru Xiong","email":"","orcid":"","institution":"Education University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Xiangxiru","middleName":"","lastName":"Xiong","suffix":""},{"id":596569236,"identity":"3d8dd335-c0b9-4f74-adb9-1135bb28191a","order_by":5,"name":"Yuqi Lu","email":"","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuqi","middleName":"","lastName":"Lu","suffix":""},{"id":596569237,"identity":"c4a2ad56-5973-41a4-a819-dea0df8e1124","order_by":6,"name":"Baohua Tan","email":"","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Baohua","middleName":"","lastName":"Tan","suffix":""},{"id":596569238,"identity":"602e1978-dd9d-404f-bc91-6175f4484985","order_by":7,"name":"Tianyu Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYBACPmYGhgMgBj/RWtiYmSFaJBuI1sLADGEYHCBaCzv/wQM/dxxO3Hz8+MMPH2oY5PnFCGgGOexg75nDidvO5BhLzjjGYDhzdgJhLQd424BabvCwMfM2MCQY3CZCy8G/QC2bZ7A/Y/5LrJbDIFs2SDCYMTMSqcXgsGxbuvEMkF96jkkQ9gs//8HHH9+2Wcv2twND7EeNjTy/NAEtUNAMY0gQpRwE6ohWOQpGwSgYBSMQAADsTD9HKAcjuQAAAABJRU5ErkJggg==","orcid":"","institution":"CSCEC Digital Technology Co., Ltd.","correspondingAuthor":true,"prefix":"","firstName":"Tianyu","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-02-14 12:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8880043/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8880043/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103507997,"identity":"f2418610-54cf-4028-9936-eb7b6cf7fb4e","added_by":"auto","created_at":"2026-02-26 13:46:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1574097,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8880043/v1_covered_4caa3cae-aecd-4809-bdc4-8e704b18f4a5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"End-to-End Deep Learning for Flight Trajectory Reconstruction from Multi-Station ADS-B Measurements","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":"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},"keywords":"ADS-B, Aircraft Trajectory Reconstruction, Deep learning, Encoder-Decoder Architecture, Neural Network","lastPublishedDoi":"10.21203/rs.3.rs-8880043/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8880043/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the field of aviation safety, ADS-B is widely adopted as an active broadcast-based aviation surveillance system, enabling aircraft to broadcast their real-time position information via onboard devices. However, the open communication protocol relied upon by ADS-B has led to increasingly frequent GPS spoofing and network hijacking attacks, posing significant threats to communication security. A reliable secondary verification method is urgently needed to revalidate the position information broadcast by aircraft. To address this issue, this paper proposes a deep learning framework that does not rely on specific message content but directly calculates aircraft trajectories through tamper-proof electromagnetic signals. Based on real flight trajectories and distributed sensor signals collected from the OpenSky dataset, we trained an End-to-End neural network, innovatively introducing heterogeneous sensor encoders and trajectory decoders, and demonstrated the effectiveness of the proposed model through empirical experiments. In comparison experiments, our proposed method achieved a breakthrough of 15.44% higher than the baseline in MDE and a single coordinate axis accuracy improvement of up to 22.77% in MRE. Finally, through ablation experiments and visualization analysis, we demonstrate the necessity of each component of the model and the overall effectiveness of trajectory reconstruction.","manuscriptTitle":"End-to-End Deep Learning for Flight Trajectory Reconstruction from Multi-Station ADS-B Measurements","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 04:31:15","doi":"10.21203/rs.3.rs-8880043/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-19T16:08:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-08T18:00:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15560445099206004166389359001413102801","date":"2026-03-01T22:16:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-01T09:25:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98993776143332061537303796645677241456","date":"2026-03-01T04:01:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47012587147981223330513248633847392040","date":"2026-02-28T11:10:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3483548638927209702417630642924188895","date":"2026-02-24T10:26:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T08:09:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-23T07:44:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-19T02:50:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-19T02:49:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-14T12:47:11+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":"532160c4-1fce-45fb-9ed8-b5291f1ca7cb","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":63478268,"name":"Physical sciences/Engineering"},{"id":63478269,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-05-05T15:39:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 04:31:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8880043","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8880043","identity":"rs-8880043","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}