Development and Testing of Multi Rotor Unmanned Aerial Vehicle Noise Source Tracking and Positioning System

doi:10.3969/j.issn.1009-086x.2024.03.001

Abstract

Abstract:

Multi-rotor drones are increasingly becoming a threat to critical facilities while bringing convenience. This article focuses on the difficulty of tracking and detecting low slow small unmanned aerial vehicles （UAVs）， and develops a UAV tracking， detection， recognition， and positioning system based on microphone array sound source localization. The system acquires UAV noise signals in real time， and then uses a wavelet transform-based beamforming time-frequency sound source localization algorithm for UAV noise source localization， and finally integrates the position of the multi-rotor UAV obtained by inversion of the microphone array and the image captured by the video. Tests conducted in a fully anechoic room have shown that the positioning system can accurately track the lateral flight and up-and-down flight of drones， accurately detect the position of drones even in blind spots of cameras， and effectively compensate for video detection. And the positioning accuracy of the system is 92.2%， which is better than that of similar systems.

Key words: unmanned aerial vehicle（UAV）, noise, microphone array, sound source localization, beamforming

摘要：

多旋翼无人机在带来便利的同时，也越来越成为重要设施的威胁。针对低慢小多旋翼无人机的跟踪探测难题，开发了一种基于麦克风阵列声源定位的多旋翼无人机跟踪探测识别定位系统。该系统实时采集无人机噪声信号，进而采用基于小波变换的波束形成时频声源定位算法进行无人机噪声源定位，最后融合了麦克风阵列反演得到的多旋翼无人机位置和视频采集到的图像。经过在全消声室环境中对多旋翼无人机进行的大量测试表明：该定位系统能准确实时跟踪定位无人机的横向飞行和上下飞行；即使在摄像头盲区也能准确探测无人机位置，有效弥补了视频探测的不足；该系统的定位精度为92.2%，优于同类系统。

关键词: 无人机, 噪声, 麦克风阵列, 声源定位, 波数形成

CLC Number:

V279

Jingyu HE, Jizheng LIU, Zhichen YANG, Dongdong WANG, Ping OU. Development and Testing of Multi Rotor Unmanned Aerial Vehicle Noise Source Tracking and Positioning System[J]. Modern Defense Technology, 2024, 52(3): 1-8.

何敬玉, 刘吉铮, 杨志晨, 汪冬冬, 欧平. 多旋翼无人机噪声源跟踪定位系统研发及测试[J]. 现代防御技术, 2024, 52(3): 1-8.

Figures/Tables 9

Fig. 1 System experiment and testing

Fig. 2 Source maps of wavelet-based beamforming

Fig. 3 Source maps of wavelet-based beamforming

Fig. 4 Spectra of multi rotor unmanned aerial vehicles noise

Fig. 5 Detection of drones when the drone is located outside of the video image

Fig. 6 Sound source localization results of multi rotor drones hovering in the camera video area

Fig. 7 Left and right horizontal motion tracking and positioning results of multi rotor unmanned aerial vehicles

Fig. 8 Vertical motion tracking and positioning results of multi rotor unmanned aerial vehicles

Fig. 9 Distribution of positioning error and accuracy

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