Fast and High-Resolution Acoustic Beamforming: A Convolution Accelerated Deconvolution Implementation

Ning Chu, Han Zhao, Liang Yu, Qian Huang, Yue Ning

科研成果: 期刊稿件文章同行评审

21 引用 (Scopus)

摘要

It is valuable to improve the resolution and speed of acoustic beamforming in real-time measurement, especially fault location, noise mapping, and so on. However, multiplication of the power transfer matrix takes much time in the iteration of the deconvolution algorithm. This article proposes a novel method based on the convolution approximation and GPU platform to obtain acoustic imaging with a high resolution quickly. First, the power propagation matrix is approximated by a symmetric Toeplitz block Toeplitz (STBT) matrix, which can transform the product of matrix and vector into the convolution of two smaller patterns. Three different convolution kernels are derived for the convolution operation, including 2-D variant convolution kernel, 2-D invariant convolution kernel, and 1-D separable invariant convolution kernel. Then, the regularization deconvolution algorithm with the convolution kernel is derived. Besides, the relation between approximation error, time cost, and setting parameters is discussed. The results show that the separable invariant convolution kernel can obtain the fastest imaging and relatively accurate localization, while the time consumption of the variant kernel is the highest. Simulations and experiments are operated on the CPU and GPU to make a comparison, which validates that our proposed deconvolution-GPU implementation can significantly improve the computation speed for the matrix on a large scale.

源语言英语
文章编号9290103
期刊IEEE Transactions on Instrumentation and Measurement
70
DOI
出版状态已出版 - 2021
已对外发布

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