典型因素对无网格压缩波束形成声源识别的影响

doi:10.3901/JME.2022.05.98

机械工程学报 ›› 2022, Vol. 58 ›› Issue (5): 98-107.doi: 10.3901/JME.2022.05.98

扫码分享

典型因素对无网格压缩波束形成声源识别的影响

杨洋^1,2, 褚志刚¹, 杨咏馨¹

1. 重庆大学机械与运载工程学院重庆 400044;
2. 重庆工业职业技术学院车辆工程学院重庆 401120

收稿日期:2021-05-07 修回日期:2021-11-22 出版日期:2022-03-05 发布日期:2022-04-28
通讯作者: 褚志刚(通信作者),男,1978年出生,博士,教授,博士研究生导师。主要研究方向为振动噪声测量分析技术、数字信号处理、噪声源识别技术理论及应用。E-mail:zgchu@cqu.edu.cn E-mail:zgchu@cqu.edu.cn
作者简介:杨洋,女,1988年出生,博士研究生,副教授。主要研究方向为噪声源识别技术理论及应用、工程信号处理。E-mail:yangyang911127@cqu.edu.cn;杨咏馨,女,1995年出生,博士研究生。主要研究方向为噪声源识别技术理论及应用、工程信号处理。E-mail:yongxinyang@cqu.edu.cn
基金资助:
重庆市教育委员会科学技术研究项目(KJQN202003206);国家自然科学基金(11874096)资助项目。

Influence of Typical Factors on Acoustic Source Identification of Grid-free Compressive Beamforming

YANG Yang^1,2, CHU Zhi-gang¹, YANG Yong-xin¹

1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044;
2. Faculty of Vehicle Engineering, Chongqing Industry Polytechnic College, Chongqing 401120

Received:2021-05-07 Revised:2021-11-22 Online:2022-03-05 Published:2022-04-28

摘要/Abstract

摘要： 基于传声器阵列测量和压缩感知理论且将目标声源区域看作连续体处理的无网格压缩波束形成声源识别方法在军事、工业、环境等领域具有良好应用前景。采用蒙特卡罗数值模拟和验证试验探究声源相干性、声源最小分离、噪声干扰和数据快拍数目四个典型因素对声源识别性能的影响，结果表明：该方法适用于任意相干性声源和任意数据快拍数目；高概率获得准确结果的条件是声源足够分离（采用矩形阵列时，单数据快拍下，通常要求声源最小分离不小于1/√AB, A和B分别为矩形阵列的行数和列数）和噪声干扰不过强（单数据快拍下，通常要求信噪比优于 15 d B）；声源不完全相干时，增多数据快拍降低对声源分离和噪声干扰强度的要求，声源完全相干时，增多数据快拍仅降低对噪声干扰强度的要求。典型因素影响的揭示对无网格压缩波束形成方法的恰当运用及声源识别结果的正确分析具有重要指导意义。

关键词: 声源识别, 无网格压缩波束形成, 典型因素, 影响

Abstract: Based on microphone array measurement and compressive sensing theory, the grid-free compressive beamforming acoustic source identification method, which treats the target area as a continuum, has good application prospects in military, industrial,environmental and other fields. Monte Carlo simulation and validation experiment are used to investigate the effects of four typical factors （source coherence, the minimum separation of sources, noise interference and the number of data snapshots） on the performance of grid-free compressive beamforming. The results show that this method is suitable for sources with arbitrary coherence and data with any number of snapshots; the conditions to obtain accurate results with high probability is that the sources are sufficiently separate （with a rectangular array and single data snapshot, the minimum separation of sources is usually required to be not less than 1/√AB, where A and B are the number of rows and columns of the rectangular array, respectively） and the noise interference is not strong （with single data snapshot, the signal-to-noise ratio is usually required to be better than 15 d B）; when the sources are not completely coherent, increasing the data snapshot will relax the requirements for source separation and the intensity of noise interference; when the sources are completely coherent, increasing the data snapshot will only relax the requirement for the intensity of noise interference. The disclosure of the influence of typical factors is of guiding significance in the proper application of grid-free beamforming and the correct analysis of the results of acoustic source identification.

Key words: acoustic source identification, grid-free compressive beamforming, typical factors, influence

中图分类号:

TB52

杨洋, 褚志刚, 杨咏馨. 典型因素对无网格压缩波束形成声源识别的影响[J]. 机械工程学报, 2022, 58(5): 98-107.

YANG Yang, CHU Zhi-gang, YANG Yong-xin. Influence of Typical Factors on Acoustic Source Identification of Grid-free Compressive Beamforming[J]. Journal of Mechanical Engineering, 2022, 58(5): 98-107.

参考文献

[1] XENAKI A,GERSTOFT P,MOSEGAARD K. Compressive beamforming[J]. Journal of the Acoustical Society of America,2014,136(1):260-271.
[2] GERSTOFT P,XENAKI A,MECKLENBRAUKER C F. Multiple and single snapshot compressive beamforming[J]. Journal of the Acoustical Society of America,2015,138(4):2003-2014.
[3] NING F,WEI J,QIU L,et al. Three-dimensional acoustic imaging with planar microphone arrays and compressive sensing[J]. Journal of Sound and Vibration,2016,380:112-128.
[4] NING F,PAN F,ZHANG C,et al. A highly efficient compressed sensing algorithm for acoustic imaging in low signal-to-noise ratio environments[J]. Mechanical Systems and Signal Processing,2018,112:113-128.
[5] BU H,HUANG X,ZHANG X. High-resolution acoustical imaging for rotating acoustic source based on compressive sensing beamforming[C]//Proceedings of the 25th AIAA/CEAS Aeroacoustics Conference,20-23 May 2019,Delft,Netherlands. AIAA-2019-2410.
[6] YANG Z,XIE L,ZHANG C. Off-grid direction of arrival estimation using sparse Bayesian inference[J]. IEEE Transactions on Signal Processing,2013,61(1):38-43.
[7] PARK Y,SEONG W,GERSTOFT P. Block-sparse two-dimensional off-grid beamforming with arbitrary planar array geometry[J]. Journal of the Acoustical Society of America,2020,147(4):2184-2191.
[8] PARK Y,GERSTOFT P. Compressive 2-D off-grid DOA estimation for propeller cavitation localization[C]//2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP),4-8 May 2020,Barcelona,Spain. 2020:4612-4616.
[9] 樊小鹏,余立超,褚志刚,等.二维动态网格压缩波束形成声源识别方法[J].机械工程学报,2020,56(22):46-55. FAN Xiaopeng,YU Lichao,CHU Zhigang,et al. Two-dimensional dynamic grid compressive beamforming for acoustic source identification[J]. Journal of Mechanical Engineering,2020,56(22):46-55.
[10] YANG Y X,CHU Z,YANG Y,et al. Two-dimensional Newtonized orthogonal matching pursuit compressive beamforming[J]. Journal of the Acoustical Society of America,2020,148(3):1337-1348.
[11] XENAKI A,GERSTOFT P. Grid-free compressive beamforming[J]. Journal of the Acoustical Society of America,2015,137(4):1923-1935.
[12] PARK Y,CHOO Y,SEONG W. Multiple snapshot grid free compressive beamforming[J]. Journal of the Acoustical Society of America,2018,143(6):3849-3859.
[13] ANG Y Y,NGUYEN N,GAN W S. Multiband grid-free compressive beamforming[J]. Mechanical Systems and Signal Processing,2020,135:106425.
[14] YANG Y,CHU Z,PING G. Two-dimensional multiple-snapshot grid-free compressive beamforming[J]. Mechanical Systems and Signal Processing,2019,124:524-540.
[15] YANG Y,CHU Z,YANG L,et al. Enhancement of direction-of-arrival estimation performance of spherical ESPRIT via atomic norm minimisation[J]. Journal of Sound and Vibration,2021,491:115758.
[16] CHI Y,SCHARF L L,PEZESHKI A,et al. Sensitivity to basis mismatch in compressed sensing[J]. IEEE Transactions on Signal Processing,2011,59(5):2182-2195.
[17] YANG Z,XIE L,STOICA P. Vandermonde decomposition of multilevel Toeplitz matrices with application to multidimensional super-resolution[J]. IEEE Transactions on Information Theory,2016,62(6):3685-3701.
[18] JO B,CHOI J W. Parametric direction-of-arrival estimation with three recurrence relations of spherical harmonics[J]. Journal of the Acoustical Society of America,2019,145(1):480-488.
[19] YANG Y,CHU Z,XU Z,et al. Two-dimensional grid-free compressive beamforming[J]. Journal of the Acoustical Society of America,2017,142(2):618-629.
[20] YANG Y,CHU Z,PING G,et al. Resolution enhancement of two-dimensional grid-free compressive beamforming[J]. Journal of the Acoustical Society of America,2018,143(6):3860-3872.
[21] LIU M,DONG C,DONG Y,et al. Superresolution 2D DOA estimation for a rectangular array via reweighted decoupled atomic norm minimization[J]. Mathematical Problems in Engineering,2019,2019:6797168.
[22] LIU Y,CHU Z,YANG Y. Iterative Vandermonde decomposition and shrinkage-thresholding based two-dimensional grid-free compressive beamforming[J]. Journal of the Acoustical Society of America, 2020,148(3):EL301-EL306.
[23] DUMITRESCU B. Positive trigonometric polynomials and signal processing applications[M]. Dordrecht, Netherlands:Springer,2007.
[24] BOYD S,VANDENBERGHE L. Convex optimization[M]. Cambridge,UK:Cambridge University Press,2004.
[25] YANG Z,XIE L. Exact joint sparse frequency recovery via optimization methods[J]. IEEE Transactions on Signal Processing,2016,64(19):5145-5157.

典型因素对无网格压缩波束形成声源识别的影响

Influence of Typical Factors on Acoustic Source Identification of Grid-free Compressive Beamforming

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	王博士, 陈楠楠, 蔡艳, 王敏. 低合金贝氏体高强钢模拟焊接粗晶热影响区低温疲劳韧脆转变行为及机制[J]. 机械工程学报, 2024, 60(6): 271-278.
[2]	高昇, 吴建军, 闫晶, 张荣霞, 喻忠平, 刘龙. 基于能量法的管件自由弯曲失稳起皱预测和变形规律研究[J]. 机械工程学报, 2024, 60(20): 162-172.
[3]	袁野, 杨怀广, 丁亮, 兰清宁, 杨超杰, 高海波, 邓宗全. 反映载荷影响的星球车车轮试验及模型研究[J]. 机械工程学报, 2024, 60(14): 263-271.
[4]	闫玉升, 钟史放, 徐连勇, 赵雷, 韩永典. 焊接热输入对X65管道粗晶热影响区应力腐蚀开裂行为的影响[J]. 机械工程学报, 2024, 60(12): 220-227.
[5]	余海东, 高畅, 赵勇, 袁可. 机械产品装配偏差分析方法研究进展与展望[J]. 机械工程学报, 2023, 59(9): 212-229.
[6]	罗忠, 刘凯宁, 刘家希, 李雷, 姜广义. 支承动刚度对转子系统临界转速的影响及试验验证[J]. 机械工程学报, 2023, 59(21): 245-255.
[7]	褚志刚, 赵洋, 杨亮, 张晋源, 杨洋. 基于稀疏贝叶斯学习的压缩球波束形成声源识别方法[J]. 机械工程学报, 2023, 59(2): 1-13.
[8]	徐亮, 权璐纯, 尚俊超, 李敬豪, 张小正. 基于源强先验引导的正交匹配追踪声源识别算法[J]. 机械工程学报, 2022, 58(24): 20-31.
[9]	朱林, 王鹏, 贾民平, 许飞云. 基于响应面修正敏度模型的结构可靠性影响因素分析方法[J]. 机械工程学报, 2022, 58(22): 160-167.
[10]	朱林, 邱建春, 陈敏. 基于修正Sobol灵敏度模型的拨穗授粉机疲劳寿命影响因素分析研究[J]. 机械工程学报, 2022, 58(16): 189-196.
[11]	于曰伟, 赵雷雷, 周长城. 坐姿人体与车辆-轨道系统的垂向耦合作用机理[J]. 机械工程学报, 2022, 58(16): 290-300.
[12]	高红, 肖杰. 波浪能捕获浮体对能量转化系统特性的影响[J]. 机械工程学报, 2022, 58(16): 360-369.
[13]	杨咏馨, 褚志刚, 杨洋. 基于正交匹配追踪的二维离网压缩波束形成声源识别方法[J]. 机械工程学报, 2022, 58(11): 88-97.
[14]	周意葱, 林起崟, 张宁静, 洪军, 杨楠. 均化接触应力的双线性材料刚度优化设计[J]. 机械工程学报, 2022, 58(1): 212-220.
[15]	靳岚, 芮执元, 蒋海元, 潘建龙. 考虑接触参数与摩擦生热交互影响的高速角接触球轴承温升预测研究[J]. 机械工程学报, 2021, 57(7): 61-67.