Ensemble Learning Models for Micro-Drone Detection Using Integrated Acoustic Signatures

Ensemble Learning Models for Micro-Drone Detection Using Integrated Acoustic Signatures | 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 Ensemble Learning Models for Micro-Drone Detection Using Integrated Acoustic Signatures Pavan Kumar Sesham, Srinu Sesham, Musa Ndiaye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8008024/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Discover Artificial Intelligence → Version 1 posted 10 You are reading this latest preprint version Abstract The rapid evolution of unmanned aerial vehicles (UAVs) has transformed both civilian and defense sectors. However, unauthorized UAV use poses significant challenges to public safety and airspace regulation, highlighting the need for robust anti-drone defense systems. Such systems aim to detect, track, and mitigate/neutralize aerial threats. Detecting drones in complex environments remains challenging due to diverse operational conditions and ambient noise. Numerous acoustic signature-based deep learning methods have been proposed for reliable micro-drone detection. While these methods outperform traditional statistical signal processing techniques in detection accuracy, they are computationally intensive and require large amounts of training data. Hence, developing lightweight techniques that require less training data while maintaining comparable detection performance on real-time edge devices is essential. To achieve this, Autocorrelation Coefficients (temporal features) and Mel-Frequency Cepstral Coefficients (spectral features) are extracted from captured acoustic data. The integrated features are applied to multiple ensemble learning models, and their detection performance is evaluated, including the Random Forest algorithm, AdaBoost model, Extreme Gradient Boosting model, and Stacking Ensemble model. Experimental analysis shows that the Stacking Ensemble model achieves detection performance comparable to deep learning models while requiring lower computational complexity and reduced training data, attaining an overall detection accuracy of 98%. These findings demonstrate that integrating acoustic features with ensemble algorithms provides a reliable and scalable framework for real-time acoustic drone detection, making it a promising solution for next-generation aerial surveillance systems. Micro-drone detection Acoustic Signatures Ensemble Learning Detection accuracy Anti-Drone defense system Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Discover Artificial Intelligence → Version 1 posted Editorial decision: Revision requested 02 Dec, 2025 Reviews received at journal 01 Dec, 2025 Reviews received at journal 29 Nov, 2025 Reviewers agreed at journal 23 Nov, 2025 Reviewers agreed at journal 22 Nov, 2025 Reviewers agreed at journal 20 Nov, 2025 Reviewers invited by journal 20 Nov, 2025 Editor assigned by journal 03 Nov, 2025 Submission checks completed at journal 03 Nov, 2025 First submitted to journal 01 Nov, 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. 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-8008024","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":549473440,"identity":"68b1c8f1-ade7-4c9c-b4c9-8723dc6217df","order_by":0,"name":"Pavan Kumar Sesham","email":"","orcid":"","institution":"Andhra University","correspondingAuthor":false,"prefix":"","firstName":"Pavan","middleName":"Kumar","lastName":"Sesham","suffix":""},{"id":549473441,"identity":"107491b4-f123-43f0-81a0-3dd597c27c4e","order_by":1,"name":"Srinu Sesham","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYJACZiCWI12LMelaEhuIVi7ff/jw54Kabekbbjc/3cC4w46wFoMbaWnSM47dzt1w55jZDcYzyURokeAxY+ZhA2q5kQDU0sZMjMPOf/7M8+92usGN9G9ALfWEtTAcyGGQ5m27nWBwIwdky2EiHHYjzUyat++24cwbOWU3EtuOE+Oww48/83y7Lc93I33bjY9t1UQ4DAUkkKphFIyCUTAKRgF2AAB94D0UwNHH8wAAAABJRU5ErkJggg==","orcid":"","institution":"University of Namibia","correspondingAuthor":true,"prefix":"","firstName":"Srinu","middleName":"","lastName":"Sesham","suffix":""},{"id":549473442,"identity":"0dbdab73-1418-4296-90c0-09c51e52b2fc","order_by":2,"name":"Musa Ndiaye","email":"","orcid":"","institution":"University of Namibia","correspondingAuthor":false,"prefix":"","firstName":"Musa","middleName":"","lastName":"Ndiaye","suffix":""}],"badges":[],"createdAt":"2025-11-01 22:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8008024/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8008024/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s44163-026-00869-1","type":"published","date":"2026-02-02T15:57:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96948940,"identity":"445d4985-6625-4bab-8384-73e0335f2c2c","added_by":"auto","created_at":"2025-11-27 22:52:40","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5449,"visible":true,"origin":"","legend":"","description":"","filename":"9cd62d4246a24baea93283c4f5f8f8a3.json","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/cba2d929ba65ff681447c84d.json"},{"id":96948938,"identity":"e9f4d887-9a86-4438-9e28-7ac28b3e7450","added_by":"auto","created_at":"2025-11-27 22:52:40","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68303,"visible":true,"origin":"","legend":"","description":"","filename":"ACCURACYvariations.png","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/d251f7e7cba710ee810398b0.png"},{"id":96948939,"identity":"cce50c24-f1e6-46c6-b9f3-4438f7a5122a","added_by":"auto","created_at":"2025-11-27 22:52:40","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":63847,"visible":true,"origin":"","legend":"","description":"","filename":"AUCvariations.png","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/b9f6e11e502987fa38bb13cc.png"},{"id":96948946,"identity":"bffef20e-63b8-4893-ba3c-f0e12dbefbb2","added_by":"auto","created_at":"2025-11-27 22:52:40","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":633639,"visible":true,"origin":"","legend":"","description":"","filename":"Blockdiagram1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/5816733543ef62310e371487.pdf"},{"id":96948942,"identity":"7db66cb7-f815-411e-8167-9adfcfc4df77","added_by":"auto","created_at":"2025-11-27 22:52:40","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":71693,"visible":true,"origin":"","legend":"","description":"","filename":"ConMAT.png","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/bec89d6cfb53e62bf919f5da.png"},{"id":96948941,"identity":"77d5fa1d-1e03-47af-aba1-6cf14b9018b1","added_by":"auto","created_at":"2025-11-27 22:52:40","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":478623,"visible":true,"origin":"","legend":"","description":"","filename":"CoverletterDAI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/20e4d458ae09b69fe6657157.pdf"},{"id":97137484,"identity":"7c66feee-be02-40e1-aaff-f6393385e6b4","added_by":"auto","created_at":"2025-12-01 09:57:49","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121523,"visible":true,"origin":"","legend":"","description":"","filename":"Ensemblemindmap.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/8e289945d92ad442c74aa53d.pdf"},{"id":96948948,"identity":"b55897fd-8616-4a12-be00-c4bb17e115ae","added_by":"auto","created_at":"2025-11-27 22:52:40","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":452700,"visible":true,"origin":"","legend":"","description":"","filename":"Ensemblemindmap.png","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/5042b63e54a41dcddee2aac6.png"},{"id":97135894,"identity":"66b7cdc2-55c6-46c7-91c5-4538aa412f26","added_by":"auto","created_at":"2025-12-01 09:54:23","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":75550,"visible":true,"origin":"","legend":"","description":"","filename":"FINALADM.drawio.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/e1bd13a51f1f594b98f86288.pdf"},{"id":97137859,"identity":"6c6fd2e2-9da5-4f85-b910-b2590cd6d751","added_by":"auto","created_at":"2025-12-01 09:58:14","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":85242,"visible":true,"origin":"","legend":"","description":"","filename":"FINALRFA.drawio.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/87ca16e5036268afe4a44018.pdf"},{"id":97136010,"identity":"05a74d8f-983c-4b80-837c-907267abf3e1","added_by":"auto","created_at":"2025-12-01 09:54:59","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33108,"visible":true,"origin":"","legend":"","description":"","filename":"MFCCMODIFIED.png","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/36b18ee80570c7eee555476c.png"},{"id":96948952,"identity":"eb879c68-f63f-4d8a-8d74-f6d0ec477daa","added_by":"auto","created_at":"2025-11-27 22:52:40","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31797,"visible":true,"origin":"","legend":"","description":"","filename":"PRcurve.png","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/c89449ed9d35c30b96a8a7a6.png"},{"id":97137354,"identity":"8619ec5a-97bb-48e8-80a3-e55334430494","added_by":"auto","created_at":"2025-12-01 09:57:37","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1613596,"visible":true,"origin":"","legend":"","description":"","filename":"Pavan2Latex.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/6424861498b081266187ef54.pdf"},{"id":97136923,"identity":"cc49b563-e6a1-4c46-9521-80718b5b62cd","added_by":"auto","created_at":"2025-12-01 09:57:08","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":42727,"visible":true,"origin":"","legend":"","description":"","filename":"ROCcurve.png","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/ac848376d660ef8ce263942b.png"},{"id":97136140,"identity":"78b8bd2b-133e-46f0-93d8-07dfa651d119","added_by":"auto","created_at":"2025-12-01 09:55:48","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37197,"visible":true,"origin":"","legend":"","description":"","filename":"autocorrelationfeature11.png","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/8c46788b9e6507604061dbc5.png"},{"id":97136110,"identity":"d06ba7a6-6f3f-408a-8633-d34085c6e4d6","added_by":"auto","created_at":"2025-12-01 09:55:35","extension":"eps","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2890,"visible":true,"origin":"","legend":"","description":"","filename":"empty.eps","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/276b62a5ae1d2f21b3ab3fcd.eps"},{"id":96948958,"identity":"d0c4aaf4-0fa1-4e8a-ae3d-afde80ba17f7","added_by":"auto","created_at":"2025-11-27 22:52:41","extension":"eps","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91593,"visible":true,"origin":"","legend":"","description":"","filename":"fig.eps","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/f4dfec65078bc8164ceb305b.eps"},{"id":97136604,"identity":"11f57a6a-c30a-459e-a1ab-fc2e51d400c3","added_by":"auto","created_at":"2025-12-01 09:56:49","extension":"aux","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8816,"visible":true,"origin":"","legend":"","description":"","filename":"snarticlePavan2.aux","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/8d476d3cc1c3ce4a35047e9d.aux"},{"id":96948956,"identity":"c31f0757-7311-4d1d-9d94-14f35838d064","added_by":"auto","created_at":"2025-11-27 22:52:41","extension":"log","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":38235,"visible":true,"origin":"","legend":"","description":"","filename":"snarticlePavan2.log","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/56c571ce17bd29e7d37f614e.log"},{"id":96948967,"identity":"279b533a-2670-42fb-9f4c-11ab83523873","added_by":"auto","created_at":"2025-11-27 22:52:41","extension":"out","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1722,"visible":true,"origin":"","legend":"","description":"","filename":"snarticlePavan2.out","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/437e068c055a3c21c09e4cf9.out"},{"id":97136520,"identity":"4d3d927b-abbf-411e-9563-714da137cd47","added_by":"auto","created_at":"2025-12-01 09:56:42","extension":"pdf","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3066658,"visible":true,"origin":"","legend":"","description":"","filename":"snarticlePavan2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/9316a782e0644fc3873c7ccd.pdf"},{"id":97137018,"identity":"e449c66a-5650-4078-907e-f86fefc15112","added_by":"auto","created_at":"2025-12-01 09:57:16","extension":"gz","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":159411,"visible":true,"origin":"","legend":"","description":"","filename":"snarticlePavan2.synctex.gz","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/275571cb60862f9c064f66b0.gz"},{"id":97137345,"identity":"594417e2-9aa6-4aa8-af83-d0a139908bf8","added_by":"auto","created_at":"2025-12-01 09:57:36","extension":"cls","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55857,"visible":true,"origin":"","legend":"","description":"","filename":"snjnl.cls","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/d25f4c720370b691e5f44857.cls"},{"id":96948968,"identity":"5db4eeda-9c83-4739-9296-2e99c6c9b2ee","added_by":"auto","created_at":"2025-11-27 22:52:41","extension":"pdf","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":418495,"visible":true,"origin":"","legend":"","description":"","filename":"usermanual.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/e44c564610fa464e899f869e.pdf"},{"id":96948950,"identity":"25340272-8257-4b74-979e-8fd9b511834d","added_by":"auto","created_at":"2025-11-27 22:52:40","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":57523,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineACCURACYvariations.png","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/3202694f6d06f48a8b748602.png"},{"id":97137339,"identity":"bd886ac2-81df-4c22-b345-6556a5ff9b4d","added_by":"auto","created_at":"2025-12-01 09:57:36","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":54110,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineAUCvariations.png","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/5cc5cfe27d0a970f619d07fe.png"},{"id":96948970,"identity":"50543f6a-c814-457c-a8e5-0833b4eca54f","added_by":"auto","created_at":"2025-11-27 22:52:41","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":61796,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineConMAT.png","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/49da62b35641ff4e1be54225.png"},{"id":96948969,"identity":"3c7ac4ca-06a6-4ed5-9a50-90721da1c36d","added_by":"auto","created_at":"2025-11-27 22:52:41","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":264560,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineEnsemblemindmap.png","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/fdf63af4eb6c237bbdc5a8de.png"},{"id":96948962,"identity":"41e5652b-9981-4074-a431-05b5359bbde6","added_by":"auto","created_at":"2025-11-27 22:52:41","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28910,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineMFCCMODIFIED.png","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/6690efb0540f00f795f6181f.png"},{"id":97137310,"identity":"8c5f2583-2bb2-437d-829b-7a3139d50041","added_by":"auto","created_at":"2025-12-01 09:57:34","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27488,"visible":true,"origin":"","legend":"","description":"","filename":"OnlinePRcurve.png","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/89c44025a4cddc465c0746f3.png"},{"id":96948961,"identity":"114adfc4-7083-4634-b8c5-523ed6c0aed9","added_by":"auto","created_at":"2025-11-27 22:52:41","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37066,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineROCcurve.png","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/85faa602feb65a2a4590fa89.png"},{"id":96948966,"identity":"24dd2f1f-abed-452d-9c4b-16ce725e3e57","added_by":"auto","created_at":"2025-11-27 22:52:41","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31786,"visible":true,"origin":"","legend":"","description":"","filename":"Onlineautocorrelationfeature11.png","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/94da537ef1a13d3c28f3cf2f.png"},{"id":96948964,"identity":"9b480edf-7858-436a-8059-776d54aef2bc","added_by":"auto","created_at":"2025-11-27 22:52:41","extension":"xml","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":97861,"visible":true,"origin":"","legend":"","description":"","filename":"9cd62d4246a24baea93283c4f5f8f8a31structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1/d0a3a8212cfbe3d0e0d6d4c2.xml"},{"id":102235540,"identity":"9bf10fa5-c986-4ef1-a124-2c26ccf3c963","added_by":"auto","created_at":"2026-02-09 16:16:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1203819,"visible":true,"origin":"","legend":"","description":"","filename":"Pavan2Latex.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8008024/v1_covered_50c3642c-da06-4de7-8929-3a213bba7eba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ensemble Learning Models for Micro-Drone Detection Using Integrated Acoustic Signatures","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Micro-drone detection, Acoustic Signatures, Ensemble Learning, Detection accuracy, Anti-Drone defense system","lastPublishedDoi":"10.21203/rs.3.rs-8008024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8008024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\nThe rapid evolution of unmanned aerial vehicles (UAVs) has transformed both civilian and defense sectors. However, unauthorized UAV use poses significant challenges to public safety and airspace regulation, highlighting the need for robust anti-drone defense systems. Such systems aim to detect, track, and mitigate/neutralize aerial threats. Detecting drones in complex environments remains challenging due to diverse operational conditions and ambient noise. Numerous acoustic signature-based deep learning methods have been proposed for reliable micro-drone detection. While these methods outperform traditional statistical signal processing techniques in detection accuracy, they are computationally intensive and require large amounts of training data. Hence, developing lightweight techniques that require less training data while maintaining comparable detection performance on real-time edge devices is essential. To achieve this, Autocorrelation Coefficients (temporal features) and Mel-Frequency Cepstral Coefficients (spectral features) are extracted from captured acoustic data. The integrated features are applied to multiple ensemble learning models, and their detection performance is evaluated, including the Random Forest algorithm, AdaBoost model, Extreme Gradient Boosting model, and Stacking Ensemble model. Experimental analysis shows that the Stacking Ensemble model achieves detection performance comparable to deep learning models while requiring lower computational complexity and reduced training data, attaining an overall detection accuracy of 98\\%. These findings demonstrate that integrating acoustic features with ensemble algorithms provides a reliable and scalable framework for real-time acoustic drone detection, making it a promising solution for next-generation aerial surveillance systems.\n","manuscriptTitle":"Ensemble Learning Models for Micro-Drone Detection Using Integrated Acoustic Signatures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-27 22:52:36","doi":"10.21203/rs.3.rs-8008024/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-02T05:55:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-02T03:16:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-29T16:31:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287960912295339697259913672374558662168","date":"2025-11-23T18:54:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256857808709131751802845534621110176176","date":"2025-11-22T12:53:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225861795452091934751064644794951246962","date":"2025-11-20T08:18:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-20T07:54:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-03T12:14:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-03T12:11:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Artificial Intelligence","date":"2025-11-01T22:08:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"707ceff7-6bf6-4988-be0a-5d84dfe883b5","owner":[],"postedDate":"November 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:13:56+00:00","versionOfRecord":{"articleIdentity":"rs-8008024","link":"https://doi.org/10.1007/s44163-026-00869-1","journal":{"identity":"discover-artificial-intelligence","isVorOnly":false,"title":"Discover Artificial Intelligence"},"publishedOn":"2026-02-02 15:57:06","publishedOnDateReadable":"February 2nd, 2026"},"versionCreatedAt":"2025-11-27 22:52:36","video":"","vorDoi":"10.1007/s44163-026-00869-1","vorDoiUrl":"https://doi.org/10.1007/s44163-026-00869-1","workflowStages":[]},"version":"v1","identity":"rs-8008024","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8008024","identity":"rs-8008024","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}