Combining Attention Mechanism with Broad Learning: A Novel Approach to electromagnetic signal Identification

Combining Attention Mechanism with Broad Learning: A Novel Approach to electromagnetic signal Identification | 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 Combining Attention Mechanism with Broad Learning: A Novel Approach to electromagnetic signal Identification WanKang Zhang, KangJian Mao, Jun Li, Hang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7235988/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 In electromagnetic signal identification, signals from different sources often exhibit only subtle differences in specific features, posing challenges for distinguishing similar signals. To address this issue, this paper proposes a broad learning approach integrated with an attention mechanism, specifically designed for electromagnetic signal identification. The method first constructs and trains a broad learning network to extract features and classify samples, and then embeds a simple attention module between the feature mapping layer and the enhancement layer. This module enables the model to focus on critical features, while its flat structure and pseudoinverse weight computation can reduce computational burdens and improve efficiency.The approach targets key types of electromagnetic signals, including radar signals, communication electromagnetic signals, and electronic warfare electromagnetic signals — all of which require rapid and accurate identification under resource constraints. Experimental results show that the method effectively enhances computational efficiency and model robustness, making it applicable to scenarios such as radar target identification, communication security verification, and electronic warfare countermeasures, where precise differentiation of similar electromagnetic signals is crucial. Broad Learning Attention Mechanism electromagnetic signal identification 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. 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-7235988","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":494063551,"identity":"55cf206d-0a7c-40c5-abdc-3750b1bf75f3","order_by":0,"name":"WanKang Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYDACHgY2hgQGBjk29vYDJGkxMObjOZNAghYGBoPEeRIOBsTpMDhz/NmDB3/+pLdJAC37UbGNCC1nG9INEtsMctukGw8w9py5TViL2XmGYxKJDUAtMgcSmBnbiNLC2CaR8McgnU0iwYBILWebgYrZDBKI12J/5hibRGKbsWEbMJAPEuUXyZ70Z5I//sjJy7e3H3zwo4IILSjgAInqR8EoGAWjYBTgAgBWDjpDNtEPcAAAAABJRU5ErkJggg==","orcid":"","institution":"Henan Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"WanKang","middleName":"","lastName":"Zhang","suffix":""},{"id":494063552,"identity":"d6c72377-bdc1-4597-b4d5-e29e2fcf8411","order_by":1,"name":"KangJian Mao","email":"","orcid":"","institution":"Henan Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"KangJian","middleName":"","lastName":"Mao","suffix":""},{"id":494063553,"identity":"51529a86-acd8-4190-a922-f94d81708429","order_by":2,"name":"Jun Li","email":"","orcid":"","institution":"Henan Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Li","suffix":""},{"id":494063554,"identity":"b8d7e29c-7a05-4a73-8f01-ebfae80f6728","order_by":3,"name":"Hang Wang","email":"","orcid":"","institution":"Henan Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-07-28 16:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7235988/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7235988/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89356606,"identity":"77879c76-8f5f-44ef-80a8-9d46d999834f","added_by":"auto","created_at":"2025-08-19 07:32:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1426795,"visible":true,"origin":"","legend":"","description":"","filename":"CombiningAttentionMechanismwithBroadLearningANovelApproachtoelectromagneticsignalIdentification.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7235988/v1_covered_8696e788-e127-427b-a9d1-1d0872bce513.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combining Attention Mechanism with Broad Learning: A Novel Approach to electromagnetic signal Identification","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":"Broad Learning, Attention Mechanism, electromagnetic signal identification","lastPublishedDoi":"10.21203/rs.3.rs-7235988/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7235988/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In electromagnetic signal identification, signals from different sources often exhibit only subtle differences in specific features, posing challenges for distinguishing similar signals. 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