Near-Field Source Localization Using Spherical Microphone Arrays

HUANG Qinghua; ZHANG Guangfei; LIU Kai

doi:10.1049/cje.2016.01.024

Volume 25 Issue 1

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HUANG Qinghua, ZHANG Guangfei, LIU Kai, “Near-Field Source Localization Using Spherical Microphone Arrays,” Chinese Journal of Electronics, vol. 25, no. 1, pp. 159-166, 2016, doi: 10.1049/cje.2016.01.024

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HUANG Qinghua, ZHANG Guangfei, LIU Kai, “Near-Field Source Localization Using Spherical Microphone Arrays,” Chinese Journal of Electronics, vol. 25, no. 1, pp. 159-166, 2016, doi: 10.1049/cje.2016.01.024

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Near-Field Source Localization Using Spherical Microphone Arrays

doi: 10.1049/cje.2016.01.024

Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200072, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61001160), Shanghai Natural Science Foundation of China (No.14ZR1415000), and Innovation Program of Shanghai Municipal Education Commission of China (No.12YZ023).

Received Date: 2014-01-10
Rev Recd Date: 2014-05-27
Publish Date: 2016-01-10

Abstract

Abstract

A new method is proposed for joint range and bearing (azimuth and elevation) estimation of multiple near-field acoustic sources using observations collected by a spherical microphone array. First, Spherical Fourier transform (SFT) is used to construct the array signal model in the spherical harmonics domain to decouple range and bearing information. Then the relation among the spherical harmonics of three adjacent degrees is exploited to build the recursive relationship of the signal subspace. Using Eigenvalue decomposition (EVD), bearings are estimated based on the eigenvalues and simultaneously the steering matrix can be represented by the signal subspace. Finally, range is estimated using the energy ratios of the elements of the steering matrix in the spherical harmonics domain. The algorithm can avoid parameter pairing and multi-dimensional searching. It has lower computational complexity than that of the Multiple signal classification (MUSIC) method. The performance is evaluated by Monte-Carlo simulations and the estimation root mean-square errors are compared to the corresponding Cramer-Rao bounds (CRBs) and those of MUSIC range estimates, which demonstrate the validity of the proposed algorithm.
- Spherical microphone array,
- Source localization,
- Near-field,
- Spherical Fourier transform (SFT),
- Spherical harmonics

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References(28)

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