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基于球谐波分解的非共形表面声场变换的声源定位

Sound Source Localization Using Non-Conformal Surface Sound Field Transformation Based on Spherical Harmonic Wave Decomposition.

作者信息

Zhang Lanyue, Ding Dandan, Yang Desen, Wang Jia, Shi Jie

机构信息

Science and Technology on Underwater Acoustic Laboratory, Harbin Engineering University, Harbin 150001, China.

College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China.

出版信息

Sensors (Basel). 2017 May 10;17(5):1087. doi: 10.3390/s17051087.

DOI:10.3390/s17051087
PMID:28489065
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5470477/
Abstract

Spherical microphone arrays have been paid increasing attention for their ability to locate a sound source with arbitrary incident angle in three-dimensional space. Low-frequency sound sources are usually located by using spherical near-field acoustic holography. The reconstruction surface and holography surface are conformal surfaces in the conventional sound field transformation based on generalized Fourier transform. When the sound source is on the cylindrical surface, it is difficult to locate by using spherical surface conformal transform. The non-conformal sound field transformation by making a transfer matrix based on spherical harmonic wave decomposition is proposed in this paper, which can achieve the transformation of a spherical surface into a cylindrical surface by using spherical array data. The theoretical expressions of the proposed method are deduced, and the performance of the method is simulated. Moreover, the experiment of sound source localization by using a spherical array with randomly and uniformly distributed elements is carried out. Results show that the non-conformal surface sound field transformation from a spherical surface to a cylindrical surface is realized by using the proposed method. The localization deviation is around 0.01 m, and the resolution is around 0.3 m. The application of the spherical array is extended, and the localization ability of the spherical array is improved.

摘要

球形麦克风阵列因其能够在三维空间中定位任意入射角的声源而受到越来越多的关注。低频声源通常采用球形近场声全息技术进行定位。在基于广义傅里叶变换的传统声场变换中，重建面和全息面是共形面。当声源位于圆柱面上时，利用球面共形变换进行定位较为困难。本文提出了一种基于球谐波分解构建传递矩阵的非共形声场变换方法，该方法可利用球形阵列数据实现球面到柱面的变换。推导了该方法的理论表达式，并对其性能进行了仿真。此外，还进行了使用随机均匀分布元件的球形阵列进行声源定位的实验。结果表明，所提方法实现了从球面到柱面的非共形面声场变换。定位偏差约为0.01 m，分辨率约为0.3 m。扩展了球形阵列的应用范围，提高了球形阵列的定位能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/41f96cd8aed9/sensors-17-01087-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/1a7f9b7b1dab/sensors-17-01087-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/54fba0536ef0/sensors-17-01087-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/3657b50868cb/sensors-17-01087-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/801f1116fcb4/sensors-17-01087-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/43335821a0b8/sensors-17-01087-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/b3bd3c39e27f/sensors-17-01087-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/fd56b0f85968/sensors-17-01087-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/11577e8a9691/sensors-17-01087-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/41f96cd8aed9/sensors-17-01087-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/1a7f9b7b1dab/sensors-17-01087-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/54fba0536ef0/sensors-17-01087-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/3657b50868cb/sensors-17-01087-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/801f1116fcb4/sensors-17-01087-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/43335821a0b8/sensors-17-01087-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/b3bd3c39e27f/sensors-17-01087-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/fd56b0f85968/sensors-17-01087-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/11577e8a9691/sensors-17-01087-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d53/5470477/41f96cd8aed9/sensors-17-01087-g009.jpg

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引用本文的文献

1
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Sensors (Basel). 2019 Jun 9;19(11):2618. doi: 10.3390/s19112618.