Huang Linsen, Song Shaoyu, Xu Zhongming, Zhang Zhifei, He Yansong
School of Automotive Engineering, Chongqing University, 174 Shazhengjie, Chongqing 400044, China.
Sensors (Basel). 2020 Dec 18;20(24):7298. doi: 10.3390/s20247298.
The acoustic imaging (AI) technique could map the position and the strength of the sound source via the signal processing of the microphone array. Conventional methods, including far-field beamforming (BF) and near-field acoustic holography (NAH), are limited to the frequency range of measured objects. A method called Bregman iteration based acoustic imaging (BI-AI) is proposed to enhance the performance of the two-dimensional acoustic imaging in the far-field and near-field measurements. For the large-scale ℓ1 norm problem, Bregman iteration (BI) acquires the sparse solution; the fast iterative shrinkage-thresholding algorithm (FISTA) solves each sub-problem. The interpolating wavelet method extracts the information about sources and refines the computational grid to underpin BI-AI in the low-frequency range. The capabilities of the proposed method were validated by the comparison between some tried-and-tested methods processing simulated and experimental data. The results showed that BI-AI separates the coherent sources well in the low-frequency range compared with wideband acoustical holography (WBH); BI-AI estimates better strength and reduces the width of main lobe compared with ℓ1 generalized inverse beamforming (ℓ1-GIB).
声学成像（AI）技术可通过对麦克风阵列进行信号处理来绘制声源的位置和强度。包括远场波束形成（BF）和近场声学全息术（NAH）在内的传统方法，受限于被测物体的频率范围。为了提高远场和近场测量中二维声学成像的性能，提出了一种基于Bregman迭代的声学成像方法（BI-AI）。对于大规模的ℓ1范数问题，Bregman迭代（BI）可获取稀疏解；快速迭代收缩阈值算法（FISTA）用于求解每个子问题。插值小波方法提取有关声源的信息并细化计算网格，以在低频范围内支持BI-AI。通过将该方法与一些经过验证的处理模拟数据和实验数据的方法进行比较，验证了该方法的性能。结果表明，与宽带声学全息术（WBH）相比，BI-AI在低频范围内能很好地分离相干声源；与ℓ1广义逆波束形成（ℓ1-GIB）相比，BI-AI能更好地估计强度并减小主瓣宽度。
