A bayesian sparse inference approach in nearfield wideband aeroacoustic imaging

Recently improved deconvolution methods using sparse reg-ularization achieve high spatial resolution in aeroacoustic imaging in the low Signal-to-Noise Ratio (SNR), but sparse prior and model parameters should be optimized to obtain super resolution and be robust to sparsity constraint. In this paper, we propose a Bayesian Sparse Inference Approach in Aeroacoustic Imaging (BSIAAI) to reconstruct both source powers and positions in poor SNR cases, and simultaneously estimate background noise and model parameters. Double Exponential prior model is selected for source spatial dis-tribution and hyper-parameters are estimated by Joint Max-imized A Posterior criterion and Bayesian Expectation and Minimization algorithm. On simulated and wind tunnel data, proposed approach is well applied for near-field wideband monopole and extended source imaging. Comparing to sev-eral classical methods, proposed approach is robust to noise, super resolution, wide dynamic range, and source number and SNR are not needed. Index Terms — Bayesian sparse inference, near-field wideband, aeroacoustic imagin