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...

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Bibliographic Details
Published in:Journal of physics. Conference series 2024-12, Vol.2922 (1), p.012003
Main Authors: Zakaria, N A Z, F N S Mohd Rozaimi, S A Enche Ab Rahim, Khan, Z I
Format: Article
Language:English
Subjects:
Acoustics
Algorithms
Data acquisition
Data analysis
Data processing
Digital filters
Drones
Radar detection
Signal analysis
Signal processing
Sound filters
Surveillance radar
Target detection
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Summary: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2922/1/012003