ISAR Pulses Reconstruction and High-resolution Imaging via Frequency-selective Reweighted Atomic Norm Minimization

ISAR Pulses Reconstruction and High-resolution Imaging via Frequency-selective Reweighted Atomic Norm Minimization | 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 ISAR Pulses Reconstruction and High-resolution Imaging via Frequency-selective Reweighted Atomic Norm Minimization Tao Zhang, Sui Wang, Ran Lai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4592051/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 Receiving incomplete signals is common in real inverse synthetic aperture radar (ISAR) imaging. If incomplete signals are used for imaging, severe grating lobes will be present in the obtained image. The gridless sparse recovery (SR) method, called atomic norm minimization (ANM), can reconstruct missing signals accurately and is particularly well-suited for sparse ISAR imaging. The Frequency-selective (FS) ANM (FSANM) method uses prior knowledge to improve the estimation performance of the ANM method. However, the estimated performance of the FSANM method is still limited by convex relaxation when frequency separation is unreasonable, which leads to unsatisfactory imaging results. To break this limitation and improve ISAR imaging performance, a novel gridless sparse ISAR imaging method was proposed, which can be called FS reweighted ANM (FSRAM). The proposed method introduces a non-convex metric to establish a connection between atomic \({\ell _0}\) norm and \({\ell _1}\) norm. According to the non-convex iterative solution model, the implementation of a semi-definite program (SDP) for FSRAM was derived. The simulated experimental results indicate that the proposed method maintains a high level of estimate performance even in cases when the frequency separation is not acceptable. The real experimental results show that the proposed method can obtain better quality ISAR images. Atomic norm minimization sparse recovery frequency-selective inverse synthetic aperture radar high-resolution Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-4592051","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":326004934,"identity":"0e8c9fa2-9be9-4000-b150-622107e91651","order_by":0,"name":"Tao Zhang","email":"","orcid":"","institution":"Civil Aviation University of China","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Zhang","suffix":""},{"id":326004935,"identity":"d44889a8-be9e-44a1-9437-74a870acc230","order_by":1,"name":"Sui Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBADHjZm5oMPEipqiNciw8fOlmzw4Mwx4rXYyPHzmEk+bGEmrNTg+NnDL3/8ATmMx6wisYGNgb+9OwG/ljN5ada8bSAtbGU3EnfIMEicObsBrxazAzlmxowNYO9vu5F4ho3BQCKXgJbzb8wMIQ5jMCtIbGMmQsuNHOMHPGwgLSxmDERpsb/xxowZ6pdkiYQzx3gI+kWyP8f4I9Bh9vL9hw9+/FFRI8ff3otfCxCwSTAw/IfzeAgpBwHmD8SoGgWjYBSMghEMABuGQRZBjN9GAAAAAElFTkSuQmCC","orcid":"","institution":"Civil Aviation University of China","correspondingAuthor":true,"prefix":"","firstName":"Sui","middleName":"","lastName":"Wang","suffix":""},{"id":326004936,"identity":"0c126972-e5e3-484f-b514-1e3304c19ba5","order_by":2,"name":"Ran Lai","email":"","orcid":"","institution":"Civil Aviation University of China","correspondingAuthor":false,"prefix":"","firstName":"Ran","middleName":"","lastName":"Lai","suffix":""}],"badges":[],"createdAt":"2024-06-17 06:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4592051/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4592051/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63283339,"identity":"14282e83-cc5c-42d1-ab82-007e754af2f8","added_by":"auto","created_at":"2024-08-26 13:15:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1035231,"visible":true,"origin":"","legend":"","description":"","filename":"ISARPulsesReconstructionandHighresolutionImagingviaFrequencyselectiveReweightedAtomicNormMinimization.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4592051/v1_covered_7bd2785c-49d0-4a9e-bb9d-760cd04746f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ISAR Pulses Reconstruction and High-resolution Imaging via Frequency-selective Reweighted Atomic Norm Minimization","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Atomic norm minimization, sparse recovery, frequency-selective, inverse synthetic aperture radar, high-resolution","lastPublishedDoi":"10.21203/rs.3.rs-4592051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4592051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReceiving incomplete signals is common in real inverse synthetic aperture radar (ISAR) imaging. If incomplete signals are used for imaging, severe grating lobes will be present in the obtained image. The gridless sparse recovery (SR) method, called atomic norm minimization (ANM), can reconstruct missing signals accurately and is particularly well-suited for sparse ISAR imaging. The Frequency-selective (FS) ANM (FSANM) method uses prior knowledge to improve the estimation performance of the ANM method. However, the estimated performance of the FSANM method is still limited by convex relaxation when frequency separation is unreasonable, which leads to unsatisfactory imaging results. To break this limitation and improve ISAR imaging performance, a novel gridless sparse ISAR imaging method was proposed, which can be called FS reweighted ANM (FSRAM). The proposed method introduces a non-convex metric to establish a connection between atomic \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\ell _0}\\)\u003c/span\u003e\u003c/span\u003e norm and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\ell _1}\\)\u003c/span\u003e\u003c/span\u003e norm. According to the non-convex iterative solution model, the implementation of a semi-definite program (SDP) for FSRAM was derived. The simulated experimental results indicate that the proposed method maintains a high level of estimate performance even in cases when the frequency separation is not acceptable. 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