Research on wideband acoustic imaging method based on sparse Bayesian learning

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.