Near-Field Source Localization Using Spherical Microphone Arrays  

作　　者：HUANG Qinghua ZHANG Guangfei LIU Kai 

机构地区：[1]Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University

出　　处：《Chinese Journal of Electronics》2016年第1期159-166,共8页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61001160);Shanghai Natural Science Foundation of China(No.14ZR1415000);Innovation Program of Shanghai Municipal Education Commission of China(No.12YZ023)

摘　　要：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 meansquare errors are compared to the corresponding CramerRao bounds(CRBs) and those of MUSIC range estimates,which demonstrate the validity of the proposed algorithm.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 meansquare errors are compared to the corresponding CramerRao bounds（CRBs） and those of MUSIC range estimates,which demonstrate the validity of the proposed algorithm.

关 键 词：Spherical microphone array Source lo-calization NEAR-FIELD Spherical Fourier transform （SFT） Spherical harmonics. 

分 类 号：TN912.3[电子电信—通信与信息系统] TN911.72[电子电信—信息与通信工程]