Robust Acoustic Imaging Based on Bregman Iteration and Fast Iterative Shrinkage-Thresholding Algorithm
| Autor: | Zhongming Xu, Yansong He, Linsen Huang, Shaoyu Song, Zhifei Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Beamforming
Microphone array Generalized inverse Computer science Main lobe 02 engineering and technology lcsh:Chemical technology 01 natural sciences Biochemistry Article Analytical Chemistry beamforming near-field acoustic holography Wavelet 0103 physical sciences 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 Electrical and Electronic Engineering Wideband sparse representation 010301 acoustics Instrumentation Shrinkage Signal processing acoustic imaging Sparse approximation Acoustic holography Atomic and Molecular Physics and Optics Bregman iteration method 020201 artificial intelligence & image processing Algorithm Optoacoustic imaging Interpolation |
| Zdroj: | Sensors, Vol 20, Iss 7298, p 7298 (2020) Sensors Volume 20 Issue 24 Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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