基于正交匹配追踪的二维离网压缩波束形成声源识别方法

doi:10.3901/JME.2022.11.088

机械工程学报 ›› 2022, Vol. 58 ›› Issue (11): 88-97.doi: 10.3901/JME.2022.11.088

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基于正交匹配追踪的二维离网压缩波束形成声源识别方法

杨咏馨^1,2, 褚志刚^1,2, 杨洋^2,3

1. 重庆大学机械传动国家重点实验室重庆 400044;
2. 重庆大学机械与运载工程学院重庆 400044;
3. 重庆工业职业技术学院车辆工程学院重庆 401120

收稿日期:2021-06-21 修回日期:2021-12-30 出版日期:2022-06-20 发布日期:2022-08-08
通讯作者: 杨洋(通信作者),女,1988年出生,副教授。主要研究方向为噪声源识别技术理论及其应用、阵列信号处理。E-mail:yangyang911127@cqu.edu.com
作者简介:杨咏馨,女,1995年出生,博士研究生。主要研究方向为阵列声源识别技术、阵列信号处理。E-mail:yongxinyang@cqu.edu.cn;褚志刚,男,1978年出生,博士,教授,博士研究生导师。主要研究方向为振动噪声测量分析技术、噪声源识别技术理论及其应用、工程信号处理。E-mail:zgchu@cqu.edu.cn
基金资助:
国家自然科学基金资助项目（11874096）

Two-dimensional Off-grid Compressive Beamforming Based on Orthogonal Matching Pursuit for Acoustic Source Identification

YANG Yongxin^1,2, CHU Zhigang^1,2, YANG Yang^2,3

1. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044;
2. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044;
3. Faculty of Vehicle Engineering, Chongqing Industry Polytechnic College, Chongqing 401120

Received:2021-06-21 Revised:2021-12-30 Online:2022-06-20 Published:2022-08-08

摘要/Abstract

摘要： 基于平面传声器阵列的传统二维在网压缩波束形成获得准确的声源识别结果需满足两个前提：一是声源波达方向与离散化网格点一致（基匹配）；二是相关的先验参数需被准确估计。然而，在实际应用中上述两个前提难以满足，此时传统二维在网压缩波束形成声源识别性能将受基不匹配和先验参数估计不准确的综合影响而显著下降。针对该问题，提出了基于正交匹配追踪的二维离网压缩波束形成声源识别方法。该方法以网格点处传递向量的一阶泰勒展开近似真实离网声源传递向量，构造以声源在网坐标、离网偏差及声源强度为未知变量的方程组，并基于正交匹配追踪和最小二乘法求解获得声源离网坐标及强度估计。仿真和试验均表明：提出方法能够有效缓解基不匹配问题，获得相比传统在网压缩波束形成方法更高的波达方向估计及源强量化精度，实现更好的声源识别性能，且享有高的计算效率；提出方法无需信噪比或正则化参数等先验参数估计，且对稀疏度估计和网格间距不敏感，声源识别性能稳健。

关键词: 声源识别, 平面传声器阵列, 压缩波束形成, 离网, 正交匹配追踪

Abstract: Two premises need to be satisfied for conventional two-dimensional (2D) on-grid compressive beamforming with a planar microphone array to obtain accurate source identification results. One is that the direction of arrivals (DOAs) of the acoustic source are consistent with the discretized grid points (basis match); the other is that the related prior parameters need to be accurately estimated. However, the above two premises are difficult to meet in practical applications. In this case, the source identification performance of conventional 2D on-grid compressive beamforming will be significantly degraded due to the combined effects of the basis mismatch issue and inaccurate estimation of prior parameters. A 2D off-grid compressive beamforming acoustic source identification method based on orthogonal matching pursuit is proposed to solve this problem. It uses the first-order Taylor expansion of the transfer vector at the grid point to approximate the real transfer vector at the off-grid source, takes the on-grid coordinates, off-grid deviations and strengths of the sources as the unknown parameters to construct the equations, and solves them by orthogonal matching pursuit and least squares method to obtain the off-grid coordinate and strength estimates of the acoustic sources. Both simulations and experiments demonstrate that the proposed method can effectively alleviate the basis mismatch issue, obtain high accuracy of DOAs and source strengths estimation, and achieve better acoustic source identification performance than the conventional on-grid compressive beamforming. It enjoys high computational efficiency. Besides, the proposed method does not require prior knowledge of signal-to-noise ratio and/or regularization parameters and is insensitive to sparsity estimation and the grid spacing, and its acoustic source identification performance is robust.

Key words: acoustic source identification, planar microphone array, compressive beamforming, off-grid, orthogonal matching pursuit

中图分类号:

TB52

杨咏馨, 褚志刚, 杨洋. 基于正交匹配追踪的二维离网压缩波束形成声源识别方法[J]. 机械工程学报, 2022, 58(11): 88-97.

YANG Yongxin, CHU Zhigang, YANG Yang. Two-dimensional Off-grid Compressive Beamforming Based on Orthogonal Matching Pursuit for Acoustic Source Identification[J]. Journal of Mechanical Engineering, 2022, 58(11): 88-97.

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基于正交匹配追踪的二维离网压缩波束形成声源识别方法

Two-dimensional Off-grid Compressive Beamforming Based on Orthogonal Matching Pursuit for Acoustic Source Identification

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