Transient Sound Field Reconstruction Based on Sparse Bayesian Learning Time-Domain Plane Wave Superposition Method

Nearfield acoustic holography plays a significant role in Cutting-edge acoustic technology, which is capable of powerful noise source identification, localization, and sound field visualization. However, due to the number of microphones is smaller than the number of sources, it is difficult to reconstruct the acoustic field accurately. A Sparse-Bayesian-Learning-based time-domain plane wave superposition method (SBL-TDPWSM) is proposed for reconstructing the instantaneous sound field generated by sound sources. In SBL-TDPWSM, the discrete state formula of time-varying pressure on the holographic plane is incorporated into the Sparse Bayesian model to iterate the hyper-parameters, and the sound pressure wavenumber spectrum on the virtual source plane will be obtained. To verify the accuracy and robustness of the method, simulations and experiments were conducted on different sound sources. The results demonstrate that the proposed method has obvious advantages compared to traditional time-domain plane wave superposition method.