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An Automated System for Sound Localization Testing in Hearing-Impaired Listeners
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A Bayesian acoustic source localization method using L-shaped sensor clusters in plate structures with local

Chenyang Zhang1, Chenning Ma1, Jinxia Liu1

  • 1Department of Acoustics and Microwave Physics, College of Physics, Jilin University, Changchun, Jilin 130012, China.

Ultrasonics
|April 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian acoustic source localization (ASL) method using L-shaped sensor clusters (LSSC) for improved structural health monitoring in plates with varying thickness. The novel approach enhances localization accuracy by mitigating wave propagation variations.

Keywords:
Acoustic source localizationBayesian inferenceL-shaped sensor clustersLamb wavesLocal thickness variationsPlates

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Area of Science:

  • Engineering
  • Materials Science
  • Physics

Background:

  • Acoustic source localization (ASL) is crucial for structural health monitoring of plate-like structures.
  • Existing ASL methods struggle with plates exhibiting local thickness variations, leading to increased localization errors.
  • Thickness variations cause changes in guided wave propagation velocity and mode conversion, complicating accurate localization.

Purpose of the Study:

  • To develop an advanced Bayesian acoustic source localization (ASL) method specifically designed for plate-like structures with local thickness variations.
  • To enhance the accuracy and reliability of ASL in challenging structural health monitoring scenarios.
  • To mitigate the impact of wave velocity variations and mode conversion on localization precision.

Main Methods:

  • A novel Bayesian acoustic source localization (ASL) method is proposed, utilizing L-shaped sensor clusters (LSSC).
  • The LSSC configuration is employed to reduce the influence of local variations in wave velocity within the plate.
  • A Bayesian inference framework is used to update initial localization results from the LSSC algorithm, incorporating cross-correlation for TDOA extraction to circumvent mode conversion effects.

Main Results:

  • The proposed Bayesian ASL method with LSSC demonstrates superior accuracy in localizing acoustic sources within plates featuring local thickness variations.
  • Finite element simulations and experimental validation confirm the enhanced performance of the new method.
  • The method outperforms traditional Bayesian localization techniques and standalone LSSC algorithms in accuracy.

Conclusions:

  • The developed Bayesian ASL method using LSSC effectively addresses the challenges posed by local thickness variations in plate-like structures.
  • This technique offers a significant improvement for structural health monitoring applications requiring precise acoustic source localization.
  • The findings highlight the potential of integrating LSSC configurations with Bayesian inference for robust ASL in complex environments.