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<p>In response to the low accuracy of acoustic imaging with small microphone arrays, an acoustic imaging approach is proposed based on Sparse Bayesian Learning. A two-layer conjugate prior structure is built to model the signal. Additionally, it leverages shared parameters across frequency bands to achieve joint utilization of multi-frequency information, thereby reducing the adverse impact of column vector correlations in the observation matrix. All variables are modeled using conjugate priors, ensuring closed-form solutions exist for the update of all model variables. The experimental results demonstrate that the proposed method effectively improves the accuracy performance of acoustic imaging.</p>

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Research on wideband acoustic imaging method based on sparse Bayesian learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月16日

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  • Zhaoyi Liao,
  • Qiang Zeng,
  • Lirong Liu,
  • Junda He,
  • Yajie Zhang

摘要

In response to the low accuracy of acoustic imaging with small microphone arrays, an acoustic imaging approach is proposed based on Sparse Bayesian Learning. A two-layer conjugate prior structure is built to model the signal. Additionally, it leverages shared parameters across frequency bands to achieve joint utilization of multi-frequency information, thereby reducing the adverse impact of column vector correlations in the observation matrix. All variables are modeled using conjugate priors, ensuring closed-form solutions exist for the update of all model variables. The experimental results demonstrate that the proposed method effectively improves the accuracy performance of acoustic imaging.