Progress in beamforming acoustic imaging based on phased microphone arrays: Algorithms and applications

被引:1
作者
Wang, Yong [1 ]
Deng, Zhi [2 ,3 ]
Zhao, Jiaxi [1 ]
Kopiev, Victor Feliksovich [4 ]
Gao, Donglai [2 ,3 ]
Chen, Wen-Li [3 ]
机构
[1] China Aerodynam Res & Dev Ctr, Key Lab Aerodynam Noise Control, Mianyang 621000, Peoples R China
[2] Harbin Inst Technol, Minist Ind & Informat Technol, Key Lab Smart Prevent & Mitigat Civil Engn Disaste, Harbin 150090, Peoples R China
[3] Harbin Inst Technol, Minist Educ, Key Lab Struct Dynam Behav & Control, Harbin 150090, Peoples R China
[4] Cent Aerohydrodynam Inst TsAGI, Dept Aeroacoust, Moscow 105005, Russia
基金
俄罗斯科学基金会; 中国国家自然科学基金;
关键词
Phased microphone arrays; Acoustic imaging; Beamforming; Source location; Time-delay estimation; NOISE SOURCE LOCALIZATION; MODAL TRANSFER-FUNCTION; BROAD-BAND NOISE; DECONVOLUTION ALGORITHMS; FREQUENCY-DOMAIN; SOUND SOURCES; CLEAN-SC; SOURCE IDENTIFICATION; SOURCE RECONSTRUCTION; BAYESIAN-APPROACH;
D O I
10.1016/j.measurement.2024.116100
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Beamforming acoustic imaging technology, utilizing phased microphone arrays, enables precise sound source localization and finds widespread application in aerodynamic wind tunnel testing, acoustic signal recognition, and mechanical fault diagnosis. This paper presents a comprehensive review of beamforming evolution, detailing its mathematical foundations and diverse applications in acoustic imaging. Various beamforming methodologies are critically analyzed using wind tunnel test data, and an overview of correction methods for external interferences and array optimization approaches is provided. Through this examination, the strengths and limitations of each method are highlighted, offering insights for future research. Additionally, potential future enhancements, including paradigm-shift approaches to advance beamforming capabilities, are explored, suggesting directions for further innovation. This review aims to establish a foundation for newcomers to the field, stimulate academic discussion, and drive ongoing research in acoustic imaging. By elucidating beamforming complexities, correction methods, and optimization techniques, this study seeks to enhance collective knowledge and support continued advancements in this technology.
引用
收藏
页数:30
相关论文
共 248 条
[81]   Discrimination of acoustic and turbulent components from aeroacoustic wall pressure field [J].
Druault, Philippe ;
Hekmati, Abbas ;
Ricot, Denis .
JOURNAL OF SOUND AND VIBRATION, 2013, 332 (26) :7257-7278
[82]   Comparison of iterative deconvolution algorithms for the mapping of acoustic sources [J].
Ehrenfried, Klaus ;
Koop, Lars .
AIAA JOURNAL, 2007, 45 (07) :1584-1595
[83]  
Ewert R., 2008, P 14 AIAA CEAS AER C, P2940
[84]  
Ewert R., 2009, 15 AIAA CEAS AER C 3, P3217
[85]   A double-step grid-free method for sound source identification using deep learning [J].
Feng, Luoyi ;
Zan, Ming ;
Huang, Linsen ;
Xu, Zhongming .
APPLIED ACOUSTICS, 2022, 201
[86]  
Finez A., 2015, FAN2015 C
[87]   Slat Noise Assessment from Airbus A340 Flyover Phased-Array Microphone Measurements [J].
Fleury, V. ;
Malbequi, P. .
AIAA JOURNAL, 2013, 51 (07) :1667-1674
[88]   Extension of deconvolution algorithms for the mapping of moving acoustic sources [J].
Fleury, Vincent ;
Bulte, Jean .
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 2011, 129 (03) :1417-1428
[89]   Improving beampatterns of two-dimensional random arrays using convex optimization [J].
Gerstoft, Peter ;
Hodgkiss, William S. .
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 2011, 129 (04) :EL135-EL140
[90]   The In-Crowd Algorithm for Fast Basis Pursuit Denoising [J].
Gill, Patrick R. ;
Wang, Albert ;
Molnar, Alyosha .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2011, 59 (10) :4595-4605