分裂增广拉格朗日收缩反卷积声源识别算法

PDF(1491 KB)

振动与冲击 ›› 2020, Vol. 39 ›› Issue (23) : 141-148.

论文

分裂增广拉格朗日收缩反卷积声源识别算法

樊小鹏1，张鑫2,3，褚志刚2,3，李丽1

作者信息 +

Split augmented lagrange contraction deconvolution algorithm for sound source identification

FAN Xiaopeng1, ZHANG Xin2,3, CHU Zhigang2,3, LI Li1

Author information +

文章历史 +

摘要

大学汽车工程学院，重庆 400044）
摘要：提出了一种新颖高效的、超高分辨率的反卷积声源识别方法，即分裂增广拉格朗日收缩(SALSA)反卷积声源识别算法。该方法利用主要声源通常具有的稀疏特性和求解大规模稀疏恢复问题的交替方向思想，在波束形成反卷积数学模型中引入一个和源强等价的分裂变量，进而建立了增广拉格朗日变量分裂声源识别数学模型，并采用SALSA来交替迭代求解该分裂模型获得声源强度。仿真和试验结果表明，该方法与经典的反卷积声源成像方法(DAMAS)相比，源强量化能力相当，还拥有更优的收敛性，在整个分析频率范围内都拥有超高的分辨率，迭代计算速度快数十倍。

Abstract

Here, a novel and efficient deconvolution algorithm with ultra-high resolution for sound source identification was proposed, it was called the split augmented Lagrange contraction deconvolution algorithm (SALCDA).In this method, the sparsity that the main sound source usually has and the idea of alternating directions for solving large-scale sparse recovery problems were used.Then a split variable equivalent to source strength was introduced into the mathematical model of beam forming deconvolution to establish the mathematical model of augmented Lagrange variable splitting for sound source identification.SALCDA was used to solve alternately and iteratively the split model, and gain the source strength.The results of simulation and tests showed that the source strength quantification ability of the proposed method is comparable to that of the classical deconvolution approach for mapping of acoustic sources (DAMAS); it has better convergence, ultra-high resolution within the whole analysis frequency range, and its iterative calculation speed is tens of times faster.

导出引用

樊小鹏1，张鑫2,3，褚志刚2,3，李丽1. 分裂增广拉格朗日收缩反卷积声源识别算法[J]. 振动与冲击, 2020, 39(23): 141-148

FAN Xiaopeng1, ZHANG Xin2,3, CHU Zhigang2,3, LI Li1. Split augmented lagrange contraction deconvolution algorithm for sound source identification[J]. Journal of Vibration and Shock, 2020, 39(23): 141-148

参考文献

[1] 李兵, 杨殿阁, 邵林, 等. 基于波束形成和双目视觉的行驶汽车噪声源识别[J]. 汽车工程, 2008, 30(10): 889-892.
Li B, Yang D, Shao L, et al. Noise sources measurement and identification for moving automobiles [J]. Automotive Engineering, 2008, 30(10): 889-892.
[2] Chu Z, Yang Y. Comparison of deconvolution methods for the visualization of acoustic sources based on cross-spectral imaging function beamforming [J]. Mechanical Systems and Signal Processing, 2014, 48(1): 404-422.
[3] 褚志刚, 杨洋. 基于非负最小二乘反卷积波束形成的发动机噪声源识别[J]. 振动与冲击, 2013, 32(23): 75-81.
    Chu Z, Yang Y. Noise source identification for an engine based on FFT-non-negative least square (NNLS) deconvolution beamforming [J]. Journal of Vibration and Shock, 2013, 32(23): 75-81.
[4] 徐亮, 胡鹏, 张永斌, 等. 可用于相干声源的快速反卷积声源成像算法[J]. 机械工程学报, 2018, 54(23): 96-106.
Xu L, Hu P, Zhang Y, et al. A fast deconvolution approach for the mapping of coherent acoustic sources [J]. Journal of Mechanical Engineering, 2018, 54(23): 96-106.
[5] 黎术, 徐中明, 贺岩松, 等. 基于函数广义逆波束形成的声源识别[J]. 机械工程学报, 2016, 52(4): 1-6.
Li S, Xu Z, He Y, et al. Sound source identification based on functional generalized inverse beamforming [J]. Journal of Mechanical Engineering, 2016, 52(4): 1-6.
[6] Mo P, Jiang W. A hybrid deconvolution approach to separate static and moving single-tone acoustic sources by phased microphone array measurements [J]. Mechanical Systems and Signal Processing, 2017, 84: 399-413.
[7] Huang X, Bai L, Vinogradov I, et al. Adaptive beamforming for array signal processing in aeroacoustic measurements [J]. The Journal of the Acoustical Society of America, 2012, 131(3): 2152-2161.
[8] Ma W, Liu X. Improving the efficiency of DAMAS for sound source localization via wavelet compression computational grid [J]. Journal of Sound and Vibration, 2017, 395: 341-353.
[9] Brooks T F, Humphreys W M. A deconvolution approach for the mapping of acoustic sources (DAMAS) determined from phased microphone arrays [J]. Journal of Sound and Vibration, 2006, 294(4): 856-879.
[10] Ehrenfried K, Koop L. Comparison of iterative deconvolution algorithms for the mapping of acoustic sources [J], AIAA Journal. 2007, 45 (7): 1584–1595.
[11] Dougherty R, Ramachandran R, Raman G. Deconvolution of sources in aeroacoustic images from phased microphone arrays using linear programming [J]. International Journal of Aeroacoustics, 2013, 12(7-8): 699-717.
[12] Lylloff O, Fernándezgrande E, Agerkvist F, et al. Improving the efficiency of deconvolution algorithms for sound source localization [J]. Journal of the Acoustical Society of America, 2015, 138(1): 172-180.
[13] Dougherty R P. Extensions of DAMAS and benefits and limitations of deconvolution in beamforming [C], Eleventh AIAA/CEAS Aeroacoustics Conference, AIAA-2005-2961.
[14] Xenaki A, Gerstoft P, Mosegaard K. Compressive beamforming [J]. Journal of the Acoustical Society of America, 2014, 136(1): 260–271.
[15] Ning F, Wei J, Qiu L, et al. Three-dimensional acoustic imaging with planar microphone arrays and compressive sensing [J]. Journal of Sound and Vibration, 2016, 380: 112-128.
[16] 宁方立, 卫金刚, 刘勇, 等. 压缩感知声源定位方法研究[J]. 机械工程学报, 2016, 52(19): 42-52.
    Ning F, Wei J, Liu Y, et al. Study on Sound Sources Localization Using Compressive Sensing [J]. Journal of Mechanical Engineering, 2016, 52(19): 42-52.
[17] 时洁, 杨德森, 时胜国, 等. 基于压缩感知的矢量阵聚焦定位方法[J]. 物理学报, 2016, 65(2), 024302: 1-11.
    Shi J, Yang D, Shi S, et al. Compressive focused beamforming based on vector sensor array [J]. Acta Physica Sinica, 2016, 65(2), 024302: 1-11.
[18] Yardibi T, Li J, Stoica P, et al. Sparsity constrained deconvolution approaches for acoustic source mapping [J]. Journal of the Acoustical Society of America, 2008, 123(5): 2631-2642.
[19] Chu N, Picheral J, Mohammad-djafari A, et al. A robust super-resolution approach with sparsity constraint in acoustic imaging [J]. Applied Acoustics, 2014, 76: 197-208.
[20] Padois T, Berry A. Orthogonal matching pursuit applied to the deconvolution approach for the mapping of acoustic sources inverse problem [J]. Journal of the Acoustical Society of America, 2015, 138(6): 3678-3685.
[21] Afonso M V, Bioucas-Dias J M, Figueiredo M A T. Fast image recovery using variable splitting and constrained optimization [J]. IEEE Transactions on Image Processing, 2010, 19(9): 2345-2356.