Physics-aware Learnable State Space Model for UAV Trajectory Prediction

Physics-aware Learnable State Space Model for UAV Trajectory Prediction | 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 Physics-aware Learnable State Space Model for UAV Trajectory Prediction Xiaofeng Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9038224/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Unmanned aerial vehicle (UAV) trajectory prediction plays an important role in autonomous flight control, path planning, and obstacle avoidance decision-making. However, the accumulation of errors in multi-step autoregressive predictions, lack of physical consistency, and real-time constraints make existing pure data-driven models limited for engineering deployment. Therefore, this paper proposes a physics-aware learnable state space prediction framework (PLSSP). Unlike directly performing black-box regression on positions, this paper models UAV states as a joint physical state vector consisting of position, velocity, and acceleration. By using residual modeling, only the dynamic perturbation terms are learned, and a maneuver-aware selective state update mechanism is incorporated to enable adaptive modeling of the changes in flight dynamics. Additionally, the propagation behavior of multi-step prediction errors is analyzed for stability, and the model's prediction performance and engineering metrics are systematically evaluated using real PX4 flight log data. Experimental results show that, while ensuring real-time inference efficiency, the proposed method outperforms several mainstream time series prediction models in terms of Average Displacement Error (ADE) and long-term prediction stability. UAV trajectory prediction learnable state space model long-term dependency modeling selective state update time series prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 05 May, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviews received at journal 02 Apr, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviewers invited by journal 23 Mar, 2026 Editor assigned by journal 19 Mar, 2026 Submission checks completed at journal 19 Mar, 2026 First submitted to journal 05 Mar, 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. 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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-9038224","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612115790,"identity":"504ffa40-fe2e-4945-96a9-7c537954e6e1","order_by":0,"name":"Xiaofeng Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYFAC5gYJBgYJBvv2xsYHQC4PH2EtjBAtBjyHmw1AWtiI1MLAYCCR3gZmENTCD3TP7YoaizxzhsS2yq85djJsDMwPH93Ao0XizMFmyzPHJIotGw623Zbdlgx0GJuxcQ4eLQYSiW2SDWwSiQ0HG9tuS25jBmrhYZMmrOUfUMthxrZiyW31RGppbJNI3HCMsY3x47bDhLWA/dLYJ5E4s4exWZpx23EeNmYCfuFvbz54s+FbXWK//POHH39uq7bnZ29++BifFhTAzAMmiVUOAow/SFE9CkbBKBgFIwYAAGrJSE6jFibqAAAAAElFTkSuQmCC","orcid":"","institution":"Jiangsu Administration Institute","correspondingAuthor":true,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-03-05 08:58:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9038224/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9038224/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105904250,"identity":"fb39c644-8f87-45bc-83aa-a0c42a1d530e","added_by":"auto","created_at":"2026-04-01 10:06:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":433196,"visible":true,"origin":"","legend":"","description":"","filename":"PhysicsawareLearnableStateSpaceModelforUAVTrajectoryPrediction202603146.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9038224/v1_covered_124dc967-083b-469c-8134-734d74c97dbb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Physics-aware Learnable State Space Model for UAV Trajectory Prediction","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":"journal-of-intelligent-and-robotic-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Intelligent \u0026 Robotic Systems](https://link.springer.com/journal/10846)","snPcode":"10846","submissionUrl":"https://submission.springernature.com/new-submission/10846/3","title":"Journal of Intelligent \u0026 Robotic Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"UAV trajectory prediction, learnable state space model, long-term dependency modeling, selective state update, time series prediction","lastPublishedDoi":"10.21203/rs.3.rs-9038224/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9038224/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Unmanned aerial vehicle (UAV) trajectory prediction plays an important role in autonomous flight control, path planning, and obstacle avoidance decision-making. 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