[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125891-en":3,"doc-seo-125891-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125891,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","AN ACOUSTIC APPROACH TO DRONE IDENTIFICATION USING MACHINE LEARNING","Drone use in contested spaces worldwide creates a critical need for fast, accurate drone detection, localization, and identification. A miniature near-resonant MEMS acoustic vector sensor developed at the Naval Postgraduate School can detect drones and estimate direction of arrival but cannot identify the sound source. This research augments the sensor with a real-time machine learning (ML) identification component. The study uses 350 drone and background audio files preprocessed into one-second overlapping windows, producing 22,488 samples with 27 extracted features, reduced to 13 via correlation and neighborhood component analysis. Thirty-two ML models were benchmarked in MATLAB. Six achieved accuracy above 90% with acceptable prediction speed; optimization did not improve accuracy or speed. The recommended integration uses a simple neural network with ReLU and the bagged trees ensemble algorithm for accuracy, speed, and multi-class extensibility.","Calhoun: The NPS Institutional Archive  \nDSpace Repository  \n\n| 2024-06\u003Cbr>AN ACOUSTIC APPROACH TO DRONE\u003Cbr>IDENTIFICATION USING MACHINE LEARNING\u003Cbr>Kenagy, Rachel M.\u003Cbr>Monterey, CA; Naval Postgraduate School |\n| --- |\n| [https://hdl.handle.net/10945/73158](https://hdl.handle.net/10945/73158) |\n\nNPS Scholarship Theses  \nThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.  \nDownloaded from NPS Archive: Calhoun  \nNAVAL POSTGRADUATE  \nSCHOOL MONTEREY, CALIFORNIA  \nTHESIS  \nAN ACOUSTIC APPROACH TO DRONE IDENTIFICATION USING MACHINE LEARNING  \nby  \nRachel M. Kenagy  \nJune 2024  \nCo-Advisors: Fabio Durante Pereira Alves  \nThor Martinsen  \nDistribution Statement A. Approved for public release: Distribution is unlimited.  \nTHIS PAGE INTENTIONALLY LEFT BLANK  \n\n| REPORT DOCUMENTATION PAGE |  |  |  | Form Approved OMB No. 0704-0188 |  |  |\n| --- | --- | --- | --- | --- | --- | --- |\n| Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC, 20503. |  |  |  |  |  |  |\n| 1. AGENCY USE ONLY (Leave blank) |  | 2. REPORT DATE\u003Cbr>June 2024 | 3. REPORT TYPE AND DATES COVERED\u003Cbr>Master’s thesis |  |  |  |\n| 4. TITLE AND SUBTITLE\u003Cbr>AN ACOUSTIC APPROACH TO DRONE IDENTIFICATION USING MACHINE LEARNING |  |  |  |  | 5. FUNDING NUMBERS |  |\n| 6. AUTHOR(S) Rachel M. Kenagy |  |  |  |  |  |  |\n| 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)\u003Cbr>Naval Postgraduate School\u003Cbr>Monterey, CA 93943-5000 |  |  |  |  | 8. PERFORMING\u003Cbr>ORGANIZATION REPORT NUMBER |  |\n| 9. SPONSORING / MONITORING AGENCY NAME(S) AND\u003Cbr>ADDRESS(ES)\u003Cbr>N/A |  |  |  |  | 10. SPONSORING / MONITORING AGENCY REPORT NUMBER |  |\n| 11. SUPPLEMENTARY NOTES The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. |  |  |  |  |  |  |\n| 12a. DISTRIBUTION / AVAILABILITY STATEMENT\u003Cbr>Distribution Statement A. Approved for public\u003Cbr>release: Distribution is unlimited. |  |  |  |  | 12b. DISTRIBUTION CODE\u003Cbr>A |  |\n| 13. ABSTRACT (maximum 200 words)\u003Cbr>Drone use in contested spaces worldwide demonstrates an urgent need for fast and accurate drone detection, localization, and identification. The miniature near-resonant MEMS acoustic vector sensor developed at NPS provides drone detection and direction of arrival but cannot identify the source of a sound. The objective of this research is to augment the sensor with a real-time identification component using machine learning (ML) . Data used in this work includes 350 drone and background audio files, pre-processed into one-second overlapping windows, resulting in 22,488 samples. Twentyseven (27) features were extracted from each sample including signal and frequency statistics and mel frequency cepstral coefficients. Correlation analysis and neighborhood component analysis were used to select useful features for ML, decreasing feature dimensionality from 27 to 13. Thirty-two (32) ML algorithms were trained and tested using standard MATLAB parameters to establish a baseline performance for a variety of algorithms. Six algorithms resulted in an accuracy above 90% with acceptable prediction speed. Optimization did not result in higher accuracy or faster prediction speed. A simple neural network","cbCailEtW8jbKK9Y","https://ap.wps.com/l/cbCailEtW8jbKK9Y","pdf",5070217,11,1,129,"English","en",105,"# Abstract\n## Problem and objective\n## Data and feature engineering\n## Feature selection and model benchmarking\n## Results and recommended integration","[{\"question\":\"What capability does the MEMS acoustic vector sensor provide, and what limitation motivates this research?\",\"answer\":\"The sensor detects drones and estimates direction of arrival, but it cannot identify the source of a sound. This limitation motivates adding a real-time ML identification component.\"},{\"question\":\"How is the audio dataset prepared for machine learning training?\",\"answer\":\"The work uses 350 drone and background audio files, preprocessed into one-second overlapping windows, yielding 22,488 samples for model training and testing.\"},{\"question\":\"Which machine learning models performed best and what is recommended for sensor integration?\",\"answer\":\"Correlation analysis and neighborhood component analysis reduced features from 27 to 13. Six algorithms reached over 90% accuracy with acceptable prediction speed; optimization did not improve results. Integration is recommended using a simple neural network with ReLU and the bagged trees ensemble algorithm for accuracy, speed, and extensibility to multi-class problems.\"}]","AN ACOUSTIC APPROACH TO DRONE IDENTIFICATION USING MACHINE LEARNING | PDF",1785901867,325,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"an-acoustic-approach-to-drone-identification-using-machine-learning","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/an-acoustic-approach-to-drone-identification-using-machine-learning/125891/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What capability does the MEMS acoustic vector sensor provide, and what limitation motivates this research?","Question",{"text":77,"@type":78},"The sensor detects drones and estimates direction of arrival, but it cannot identify the source of a sound. This limitation motivates adding a real-time ML identification component.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How is the audio dataset prepared for machine learning training?",{"text":82,"@type":78},"The work uses 350 drone and background audio files, preprocessed into one-second overlapping windows, yielding 22,488 samples for model training and testing.",{"name":84,"@type":75,"acceptedAnswer":85},"Which machine learning models performed best and what is recommended for sensor integration?",{"text":86,"@type":78},"Correlation analysis and neighborhood component analysis reduced features from 27 to 13. Six algorithms reached over 90% accuracy with acceptable prediction speed; optimization did not improve results. 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