CLEAN-SC波束形成声源识别改进

摘要
图/表
参考文献(28)
相关文章 (15)

全文: PDF (2488 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要 CLEAN-SC波束形成声源识别方法计算速度快、成像干净清晰、结果准确度高，但当传统延迟求和算法在各声源处输出的主瓣严重融合时，亦无法准确分辨声源。造成该缺陷的原因为：主瓣严重融合时，CLEAN-SC所基于的延迟求和输出峰值所在聚焦点即为声源点的假设不成立。从源相干性角度，若某聚焦点处的延迟求和输出主要由某声源贡献时，该聚焦点可标示该声源，即基于该聚焦点的位置及强度信息可重构该声源在各传声器处产生声压的互谱矩阵。鉴于此，本文以CLEAN-SC识别的声源为初值迭代寻找正确的声源位置及强度，每次迭代中，最小化其余声源与某一声源的波束形成贡献的比值为每个声源选择标示点，根据标示点更新声源。仿真及试验均证明：所给方法比传统CLEAN-SC具有更高分辨率，使近距离低频率声源的准确识别变得可行。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	褚志刚1
	2
	余立超1
	杨洋1
	富丽娟2
	陈旭2

关键词 ：声源识别, 波束形成, CLEAN-SC, 改进

Abstract：Acoustic source identification method based on CLEAN-SC beam forming has advantages of fast calculation speed, clear images and higher accuracy.However, it can’t recognize acoustic sources when main lobes output with the traditional delay-summing algorithm at various acoustic sources are seriously fused.The reason is that when main lobes are seriously fused, the assumption that the focus point of peak values from delay-summing output is the acoustic source point is not valid, while CLEAN-SC is based on this assumption.From the view point of source coherence, a focus point can be marked an acoustic source if delay-summing output at the focus point is mainly contributed by a certain acoustic source.The cross-spectral matrix of acoustic pressures at various microphones induced by the acoustic source can be reconstructed with the location and intensity information of this focus point.Here, the identified acoustic source based on CLEAN-SC was taken as the initial value to iterate and seek the correct acoustic source location and intensity.In each iteration, the ratio of contribution of each other source’s beam forming to that of a certain acoustic source’s one was minimized and used to select the mark point for each source, and sources were updated according to their mark points.Simulations and tests showed that compared with the traditional CLEAN-SC, the proposed method has higher resolution to make the correct identification of closely spaced and lower frequency acoustic sources become feasible.

Key words： acoustic source identification beamforming CLEAN-SC improvement

收稿日期: 2018-03-14 出版日期: 2019-07-28

引用本文:

褚志刚1,2，余立超1，杨洋1，富丽娟2，陈旭2. CLEAN-SC波束形成声源识别改进[J]. 振动与冲击, 2019, 38(15): 87-94.
CHU Zhigang1,2, YU Lichao1, YANG Yang1, FU Lijuan2, CHEN Xu2. Improved acoustic source identification based on CLEAN-SC beam forming. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(15): 87-94.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I15/87

[1] MERINO-MARTINEZ R, SIJTSMA P, SNELLEN M, et al. A review of acoustic imaging methods using phased microphone arrays [J]. CEAS Aeronautical Journal, 2017, 8:1-49.
[2] 褚志刚，段云炀，沈林邦，等. 函数波束形成声源识别性能分析及应用[J]. 机械工程学报，2017, 53(4): 67-76.
CHU Zhigang, DUAN Yunyang, SHEN Linbang, et al. Performance analysis and application of functional beamforming sound source identification [J]. Journal of Mechanical Engineering, 2017, 53(4): 67-76.
[3] 郑恩明，黎远松，余华兵，等. 基于高阶次Cross Sensor处理的波束形成方法[J]. 振动与冲击, 2015, 34(24): 12-19.
ZHENG Enming, LI Yuansong, YU Huabing, et al. The beamforming method based on high order Cross Sensor processing [J]. Journal of Vibration and Shock, 2015, 34(24): 12-19.
[4] NING F L, WEI J G, QIU L F, et al. Three-dimensional acoustic imaging with planar microphone arrays and compressive sensing [J]. Journal of Sound and Vibration, 2016, 380: 112-128.
[5] 宁方立, 卫金刚, 刘勇, 等. 压缩感知声源定位方法研究[J]. 机械工程学报, 2016, 52(19): 42-52.
NING Fangli, WEI Jingang, LIU Yong. Study on sound sources localization using compressive sensing [J]. Journal of Mechanical Engineering, 2016, 52(19): 42-52.
[6] 陈思, 张志飞, 徐中明, 等. 基于高阶矩阵函数的广义逆波束形成改进算法[J]. 振动与冲击, 2017, 36(10): 98-103.
CHEN Si, ZHANG Zhifei, XU Zhongming, et al. Modified algorithm for generalized inverse beamforming based on high-order matrix function [J]. Journal of Vibration and Shock, 2017, 36(10): 98-103.
[7] BROOKS T F, HUMPHREYS W M. A deconvolution approach for the mapping of acoustic sources (DAMAS) determined from phased microphone arrays [J]. Journal of Sound and Vibration, 2006, 294(4): 856-879.
[8] CHU Z G, YANG Y. Comparison of deconvolution methods for the visualization of acoustic sources based on cross-spectral imaging function beamforming [J]. Mechanical Systems and Signal Processing, 2014, 48(1-2): 404-422.
[9] EHRENFRIED K, KOOP L. Comparison of iterative deconvolution algorithms for the mapping of acoustic sources [J]. AIAA Journal, 2007, 45(7): 1584-1595.
[10] RICHARDSON W H. Bayesian-based iterative method of image restoration [J]. Journal of the Optical Society of America, 1972, 62(1): 55-59.
[11] LUCY L B. An iterative technique for the rectification of observed distributions [J]. The Astronomical Journal, 1974, 79(6): 745-754.
[12] LYLLOFF O, FERNANDEZ-GRANDE E, AGERKVIST F, et al. Improving the efficiency of deconvolution algorithms for sound source localization [J]. Journal of the Acoustical Society of America, 2015, 138(1): 172-180.
[13] DOUGHERTY R P, RAMACHANDRAN R C, RAMAN G. Deconvolution of sources in aeroacoustic images from phased microphone arrays using linear programming [C]// 19th AIAA/CEAS Aeroacoustics Conference, Berlin, Germany, May 27-29, 2013, AIAA 2013-2210.
[14] RAMACHANDRAN R C, RAMAN G, DOUGHERTY R P. Wind turbine noise measurement using a compact microphone array with advanced deconvolution algorithms [J]. Journal of Sound and Vibration, 2014, 333(14): 3058-3080.
[15] YARDIBI T, LI J, STOICA P, et al. Sparsity constrained deconvolution approaches for acoustic source mapping [J]. Journal of the Acoustical Society of America, 2008, 123(5): 2631-2642.
[16] YARDIBI T, ZAWODNY N S, BAHR C, et al. Comparison of microphone array processing techniques for aeroacoustic measurements [J]. International Journal of Aeroacoustics, 2010, 9(6): 733-762.
[17] DOUGHERTY R P. Extensions of DAMAS and benefits and limitations of deconvolution in beamforming [C]//11th AIAA/CEAS Aeroacoustics Conference, Monterey, California, May 23-25, 2005, AIAA-2005-2961.
[18] 杨洋，褚志刚，江洪，等. 反卷积DAMAS2波束形成声源识别研究[J].仪器仪表学报，2013, 34(8): 1779-1786.
YANG Yang, CHU Zhigang, JIANG Hong, et al. Research on DAMAS2 beamforming sound source identification [J]. Chinese Journal of Scientific Instrument, 2013, 34(8): 1779-1786.
[19] YANG Y, CHU Z G, SHEN L B, et al. Enhancement of two-dimensional acoustic source identification with Fourier-based deconvolution beamforming [J]. Journal of Vibroengineering, 2016, 18(5): 3337-3361.
[20] CHU Z G, SHEN L B, YANG Y, et al. Non-negative least squares deconvolution method for mirror-ground beamforming [J]. Journal of Vibration and Control, 2016, 22(16): 3470-3478.
[21] 褚志刚，杨洋. 基于非负最小二乘反卷积波束形成的发动机噪声源识别[J]. 振动与冲击，2013, 32(23): 75-81.
CHU Zhigang, YANG Yang. Noise source identification for an engine based on Non-Negative Least Squares deconvolution beamforming [J]. Journal of Vibration and Shock, 2013, 32(23): 75-81.
[22] CHU Z G, CHEN C H, YANG Y, et al. Improvement of Fourier-based fast iterative shrinkage-thresholding deconvolution algorithm for acoustic source identification [J]. Applied Acoustics, 2017, 123: 64-72.
[23] SHEN L B, CHU Z G, YANG Y, et al. Periodic boundary based FFT-FISTA for sound source identification [J]. Applied Acoustics, 2018, 130: 87-91.
[24] SIJTSMA P. CLEAN based on spatial source coherence [J]. International Journal of Aeroacoustics, 2007, 6(4): 357-374.
[25] HALD J, KURODA H, MAKIHARA T, et al. Mapping of contributions from car-exterior aerodynamic sources to an in-cabin reference signal using Clean-SC [C]// Proceedings of the INTER-NOISE 2016, Hamburg, Germany, August 21-24, 2016: 5821-5832.
[26] 李征初，李勇，陈正武，等. CLEAN-SC算法在风洞声源定位与识别中的应用研究[J].实验流体力学，2016, 30(3): 104-109.
LI Zhenchu, LI Yong, CHEN Zhengwu, et al. Noise identification and localization in wind tunnel using CLEAN-SC algorithm [J]. Journal of Experiments in Fluid Mechanics, 2016, 30(3): 104-109.
[27] 杨洋，褚志刚. 基于CLEAN-SC清晰化波束形成的汽车前围板隔声薄弱部位识别[J].声学技术，2015, 34(5): 449-456.
YANG Yang, CHU Zhigang. Weak position identification of sound insulation for car dash panel based on CLEAN-SC clearness beamforming [J]. Technical Acoustics, 2015, 34(5): 449-456.
[28] SIJTSMA P, SNELLEN M. High-resolution CLEAN-SC [C]// 6th Berlin Beamforming Conference, Berlin, Germany, February 29-March 1, 2016, BeBeC-2016-S1.