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Robust Acoustic Imaging Based on Bregman Iteration and Fast Iterative Shrinkage-Thresholding Algorithm.

Linsen Huang1, Shaoyu Song1, Zhongming Xu1

  • 1School of Automotive Engineering, Chongqing University, 174 Shazhengjie, Chongqing 400044, China.

Sensors (Basel, Switzerland)
|December 23, 2020
PubMed
Summary
This summary is machine-generated.

A new Bregman iteration based acoustic imaging (BI-AI) method improves sound source localization accuracy. This advanced technique enhances low-frequency acoustic imaging performance in both near-field and far-field measurements.

Keywords:
Bregman iteration methodacoustic imagingbeamformingnear-field acoustic holographysparse representation

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Area of Science:

  • Acoustics
  • Signal Processing
  • Computational Imaging

Background:

  • Conventional acoustic imaging methods like beamforming (BF) and near-field acoustic holography (NAH) have frequency limitations.
  • Accurate mapping of sound source position and strength is crucial in various applications.

Purpose of the Study:

  • To introduce and validate a novel Bregman iteration based acoustic imaging (BI-AI) method.
  • To enhance the performance of 2D acoustic imaging in both far-field and near-field scenarios, particularly at low frequencies.

Main Methods:

  • Developed a BI-AI technique utilizing Bregman iteration (BI) for sparse solutions and fast iterative shrinkage-thresholding algorithm (FISTA) for sub-problems.
  • Employed the interpolating wavelet method to extract source information and refine computational grids for low-frequency analysis.
  • Validated the method using simulated and experimental data, comparing it against established techniques.

Main Results:

  • BI-AI demonstrated superior separation of coherent sound sources in the low-frequency range compared to wideband acoustical holography (WBH).
  • The BI-AI method provided more accurate source strength estimation and reduced the main lobe width compared to ℓ1 generalized inverse beamforming (ℓ1-GIB).

Conclusions:

  • Bregman iteration based acoustic imaging (BI-AI) offers enhanced performance for 2D acoustic imaging.
  • The proposed BI-AI method effectively addresses limitations of conventional techniques in low-frequency acoustic source mapping.