Research on wideband acoustic imaging method based on sparse Bayesian learning
Year: 2026
Type: article
Abstract: 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.
Source: Discover Artificial Intelligence
Institution: Guangdong Power Grid Company (China)
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W7128967631