A two-dimensional block sparse Bayesian learning acoustic imaging method with coupling prior hyperparameter

In order to solve the acoustic imaging problem of two-dimensional block sparse sound sources, a sparse Bayesian learning algorithm with coupling prior hyperparameter is proposed. For two-dimensional block sparse sound source, a Bayesian hierarchical model is established by using parametric coupling method. The sparsity of the sound source within the block is controlled by utilizing coupling constraints, which encourages a block sparse solution. The expectation maximization (EM) algorithm is used to update the hyperparameters iteratively to obtain the sound pressure distribution of the target plane. Numerical simulation experiments of sparse sound sources with different structures are designed to compare the performance of the proposed algorithm with existing algorithms. The effects of model parameters, adjacent regions, sound source size and signal-to-noise ratio on the performance of the algorithm are analyzed and acoustic imaging experiments are conducted. The simulation and experimental results show that the proposed method achieves high accuracy performance in the acoustic imaging, exhibits superior performance in sparse sound sources with different structural blocks, and can effectively solve the acoustic imaging problem of two-dimensional sparse sound sources.