A Distributed Real-Time Location System for Automobile Whistle Adaptive to Moving Sound Source
-
摘要: 针对违章鸣笛车辆的定位问题,提出了一种基于分布式传声器阵列的运动声源快速定位系统。采用GNSS时钟实现传声器间的时间同步,并将同步采集的声音信息传送到云端数据库,应用云计算技术实现声源定位算法。相比于集中式传声器阵列,该系统可大幅降低传声器的数量和运算资源,具有成本经济、部署灵活的优点。采用基于到达时间差-到达频率的快速定位算法,充分利用多普勒效应导致的分布式传声器之间到达频率差异信息来克服到达时间差法难以适应运动声源的瓶颈,避免了计算复杂、运算量大的消除多普勒效应过程,具有运算复杂度低、且能适应高速运动声源的优点。系统仿真和现场实验结果均表明该系统能够实现对高速运动声源的快速精确定位,可较好地适用于汽车鸣笛声定位场景。Abstract: Aiming at the localization problem of illegal whistle vehicles, a fast location system of moving sound source based on distributed microphone array is proposed. GNSS clock is used to realize the time synchronization between microphones, and the sound information collected synchronously is transmitted to the cloud database. Meanwhile, cloud computing technology is applied to realize the sound source localization algorithm. Compared with the centralized microphone array, it can greatly reduce the number of microphones and computing resources, and has the advantages of cost economy and flexible deployment. Besides, the proposed system adopts a fast location algorithm based on the arrival time difference and arrival frequency, which can make full use of the arrival frequency difference information between distributed microphones caused by Doppler effect to overcome the bottleneck of the arrival time difference method that is difficult to adapt to moving sound source. This proposed method can avoid the process of eliminating Doppler effect with complex calculation and large amount of calculation, and has low computational complexity and can adapt to high-speed moving sound source. Finally, the system simulation and field experiment results show that the proposed system can realize the fast and accurate positioning of high-speed moving sound source, and can be better applied to the scene of car whistle positioning.
-
表 1 传声器数量对定位精度的影响
Table 1. Influence of microphone numbers on positioning accuracy
Number of microphone Static error /m Motion error /m 3 0.479 2.463 4 0.428 0.601 5 0.415 0.589 6 0.397 0.576 表 2 网格大小对定位精度和计算耗时的影响
Table 2. Influence of grid size on positioning accuracy and computing time
Grid size/m2 Location error/m Computing time/ms 0.2×0.2 0.3242 281 0.5×0.5 0.4160 48 1.0×1.0 0.7496 13 表 3 实验结果
Table 3. Experimental results
v/ (km·h−1) TDOA-FOA FMTDOA Location
error /mCalculation
time /msLocation
error /mCalculation
time /s0 0.7 65 null null 10 1.9 68 1.1 40.7 30 1.7 68 1.2 42.6 50 1.2 72 1.5 43.5 70 0.8 69 1.7 42.8 -
[1] LIU J P, ZHANG Y W, LIU Y. Recognition and localization of car whistles using the microphone array[J]. Journal of Xidian University, 2012, 39(1): 163-167. [2] YU L, ANTONI J, WU H J, et al. Fast iteration algorithms for implementing the beamforming of non-synchronous measurements[J]. Mechanical Systems and Signal Processing, 2019, 134: 106309. [3] 陶文俊, 郑明辉. 基于等效源法的近场声全息的噪声源识别与定位研究[J]. 计算机与数字工程, 2019, 47(7): 1672-1677. doi: 10.3969/j.issn.1672-9722.2019.07.023 [4] KRALJEVIC L, RUSSO M, STELLA M, et al. Free-field TDOA-AOA sound source localization using three soundfield microphones[J]. IEEE Access, 2020, 8: 87749-87761. doi: 10.1109/ACCESS.2020.2993076 [5] CHIARIOTTI P, MARTARELLI M, CASTELLINI P. Acoustic beamforming for noise localization-review, methodology and applications[J]. Mechanical Systems and Signal Processing, 2019, 120: 422-448. doi: 10.1016/j.ymssp.2018.09.019 [6] MENG F Y, LI Y, MASIERO B, et al. Signal reconstruction of fast moving sound sources using compressive beamforming[J]. Applied Acoustics, 2019, 150: 236-245. doi: 10.1016/j.apacoust.2019.02.012 [7] ZHANG C Q, GAO Z Y, CHEN Y Y, et al. Locating and tracking sound sources on a horizontal axis wind turbine using a compact microphone array based on beamforming[J]. Applied Acoustics, 2019, 146: 295-309. doi: 10.1016/j.apacoust.2018.10.006 [8] NING F L, SONG J H, HU J L, et al. Sound source localization of non-synchronous measurements beamforming with block Hermitian matrix completion[J]. Mechanical Systems and Signal Processing, 2021, 147: 107118. doi: 10.1016/j.ymssp.2020.107118 [9] 袁芳, 闫建伟, 张勇, 等. 汽车鸣笛声实时抓拍的理论研究和系统实现[J]. 电声技术, 2018(11): 13-15, 21. [10] CHELLIAH K, RAMAN G, MUEHLEISEN R T. An experimental comparison of various methods of nearfield acoustic holography[J]. Journal of Sound and Vibration, 2017, 403: 21-37. doi: 10.1016/j.jsv.2017.05.015 [11] AUJOGUO N, ROSS A, ATTENDU J M. Time-space domain nearfield acoustical holography for visualizing normal velocity of sources[J]. Mechanical Systems and Signal Processing, 2020, 139: 106363. doi: 10.1016/j.ymssp.2019.106363 [12] VALDIVIA N P. Krylov subspace iterative methods for time domain boundary element method based nearfield acoustical holography[J]. Journal of Sound and Vibration, 2020, 484: 115498. doi: 10.1016/j.jsv.2020.115498 [13] 张揽月, 丁丹丹, 杨德森, 等. 阵元随机均匀分布球面阵列联合噪声源定位方法[J]. 物理学报, 2017, 66(1): 140-151. [14] BOORA R, DHULL S K. A TDOA-based multiple source localization using delay density maps[J]. Sadhana-Academy Proceedsings in Engineering Sciences, 2020, 45(1): ; 204. [15] 张焕强, 黄时春, 蒋伟康. 基于传声器阵列的汽车鸣笛声定位算法及实现[J]. 噪声与振动控制, 2018, 38(3): 10-14. doi: 10.3969/j.issn.1006-1355.2018.03.002 [16] 杨殿阁, 张凯, 苗丰, 等. 运动声源快速定位的声达时差法[J]. 声学学报, 2020, 45(1): 69-76. [17] ADRITYA H B B C H, SAPUTRA H M. Azimuth estimation based on generalized cross correlation phase transform (GCC-PHAT) using equilateral triangle microphone array[C]// 2019 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET). Tangerang, Indonesia: IEEE, 2019: 89-93. [18] ZHU M X, WANG Y B, CHANG D G, et al. Quantitative comparison of partial discharge localization algorithms using time difference of arrival measurement in substation[J]. International Journal of Electrical Power & Energy systems, 2019, 104: 10-20. -