Signal analysis for drone detection and characterization using acoustic radar

In response to the growing use of drones, this paper focuses on developing a radar-based drone detection system, emphasizing the challenges and opportunities of the Acoustic Radar method. The research addresses the critical challenge of accurately detecting drones to protect sensitive areas and faci...

وصف كامل

التفاصيل البيبلوغرافية
الحاوية / القاعدة:Journal of Physics: Conference Series
المؤلف الرئيسي: Zakaria N.A.Z.; Rozaimi F.N.S.M.; Ab Rahim S.A.E.; Khan Z.I.
التنسيق: Conference paper
اللغة:English
منشور في: Institute of Physics 2024
الوصول للمادة أونلاين:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214358011&doi=10.1088%2f1742-6596%2f2922%2f1%2f012003&partnerID=40&md5=3db8816e0d4672ed7b48bd55fd4c8610
الوصف
الملخص:In response to the growing use of drones, this paper focuses on developing a radar-based drone detection system, emphasizing the challenges and opportunities of the Acoustic Radar method. The research addresses the critical challenge of accurately detecting drones to protect sensitive areas and facilities. By tackling the limitations of current drone detection methods, such as optical surveillance, and addressing challenges related to limited datasets, this research innovates analysis for accurate and reliable drone detection and characterization. The methodology involves data acquisition, signal processing using MATLAB, and signal analysis. The system configuration includes wired microphones attached to a tripod and connected to a laptop for data processing and display, set up in an outdoor environment to minimize interference. Quantitative insights are derived through mean and standard deviation calculations, guided by predefined targets reflecting amplitude variations based on drone size and distance. To address the challenge of surrounding sound noise, the system implements digital filters to enhance detection accuracy. The results underscore the effectiveness of the proposed Acoustic Radar method in advancing algorithms for accurate and reliable drone detection and characterization. © 2024 Institute of Physics Publishing. All rights reserved.
تدمد:17426588
DOI:10.1088/1742-6596/2922/1/012003