基于声压球谐函数分解的球面波束形成噪声源识别

基于声压球谐函数分解的球麦克风阵列波束形成算法能够同时对三维空间所有方向进行声源定位，特别适用于内场噪声源的识别。该文以典型的50通道刚性球麦克风阵列为例，进行了声源识别性能仿真分析，结果表明：球谐函数截断长度、声源频率和声源位置等参数对阵列响应均具有显著影响，2 000 Hz对应的声压球谐函数最优截断长度为5，且所有声源位置的最大旁瓣水平的最大值可达-15.35 dB。在此基础上，开发了阵列动力学性能分析及声源识别成像软件。利用该软件对已知声源的试验算例进行声源成像，成像结果与声源真实位置吻合，表明该算法能准确识别声源，证实了自主研发程序的正确性。

Noise source identification by spherical beamforming based on sound pressure spherical harmonics decomposition

The beamforming algorithm for spherical microphone arrays based on sound pressure spherical harmonics decomposition can identify simultaneously noise sources from all directions in three-dimensional space, especially in cabin. A performance simulation of noise source identification was carried out for the typical rigid spherical array with 50 microphones. The results showed that the truncated length of the spherical harmonics, frequency and position of the noise sources had significant effects on the response of arrays, the optimal truncated length was 5 and the maximum MSL was up to -15.35 dB at 2 000 Hz. Furthermore, software for analyzing the dynamic performance of spherical array and imaging the acoustic sources was developed. The test example imaging of a given source using the software proves that the algorithm and the software is accurate.