基于互谱矩阵函数的多声源识别方法  

Multi-source Identification Method Based on Cross-Spectral Matrix Function

作　　者：王月 杨超 王岩松 胡定玉[2] 顾汝彬 WANG Yue;YANG Chao;WANG Yansong;HU Dingyu;GU Rubin(School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science Shanghai,201620,China;School of Urban Railway Transportation,Shanghai University of Engineering Science Shanghai,201620,China;The 32128 Troops of the Chinese People′s Liberation Army Jinan,250000,China;Shanghai Yangpu Vocational and Technical School Shanghai,200093,China)

机构地区：[1]上海工程技术大学机械与汽车工程学院,上海201620 [2]上海工程技术大学城市轨道交通学院,上海201620 [3]中国人民解放军32128部队,济南250000 [4]上海杨浦职业技术学校,上海200093

出　　处：《振动．测试与诊断》2023年第2期277-281,408,共6页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(51675324,52172371);上海高校青年教师培养计划资助项目(ZZGCD18019)。

摘　　要：为解决多声源识别中的混叠问题,提出一种基于互谱矩阵函数的多声源识别方法。首先,对与各峰值源相关的互谱矩阵进行特征值分解,并利用特征值和特征向量构造其函数;其次,在反卷积迭代过程中,将该函数用于计算移除各峰值源后的互谱矩阵函数和输出功率;最后,将与各峰值源相关的干净波束和剩余功率谱叠加,获得多声源的分布图像。仿真与实验结果表明,该算法的输出波束主瓣窄、旁瓣低,可有效提升传统反卷积算法的动态范围,实现多声源的高分辨率识别。In order to solve the aliasing problem in multi-source identification,a high-resolution deconvolution algorithm based on cross-spectral matrix function is proposed.Firstly,the algorithm decomposes the cross-spectral matrix associated with each peak source into eigenvalues and constructs a matrix function.Secondly,the matrix function is used to update the cross-spectral matrix function and output power in the process of deconvolution iteration.Finally,the distribution images of multiple sound sources are obtained by superimposing each clean beam and residual power spectrum.Simulations and experiments show that the main lobe of the output beam of the algorithm is narrow and the side lobe is low.This algorithm can not only effectively improve the dynamic range of the traditional deconvolution algorithm,but also achieve high-resolution identification of multiple sound sources.

关 键 词：声源识别 波束形成 特征值分解 反卷积 

分 类 号：TH17[机械工程—机械制造及自动化] TB52[理学—物理]