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A Bayesian Grid-Free Framework with Global Optimization for Three-Dimensional Acoustic Source Imaging
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DOI：10.3390/app152011028.png)
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Abstract
En 中文
A common challenge in traditional three-dimensional grid-free localization is the struggle to balance computational efficiency with localization accuracy. To address this trade-off, a Bayesian grid-free framework with global optimization (BGG) for three-dimensional acoustic source imaging is proposed. In this method, a Bayesian inference model is established based on equivalent source theory, where the negative log-posterior of the equivalent source positions serves as the fitness function. This function is minimized using a global optimization algorithm to estimate the source locations. Subsequently, the source strengths and noise variances are inferred via fixed-point iteration and projection-based estimation. Through both simulations and experiments with spatially distributed sources, a superior balance of computational efficiency and localization accuracy is demonstrated by the proposed BGG algorithm when compared to other state-of-the-art grid-free approaches.
Keywords:
3D acoustic source imaging
grid-free method
Bayesian inference
global optimization
array measurement
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