Bayesian sparse regularization in near-field wideband aeroacoustic imaging for wind tunnel test

نویسندگان

  • N. Chu
  • J. Picheral
  • A. Djafari
چکیده

1397 Robust deconvolution-based methods using sparsity constraint and sparse regularization achieve high spatial resolutions in aeroacoustic imaging in low Signal-to-Noise Ratio (SNR). But sparse prior and model parameters should be further optimized to obtain super resolution and be robust to sparsity constraint. In this paper, we propose a Robust Approach with Bayesian Sparse Regularization in Aeroacoustic Imaging (RABSRAI) to detect both positions and powers of near-field wideband uncorrelated sources in poor SNR case, and simultaneously estimate background noise. The Bayesian interpretation is applied to select the sparse prior and regularization parameter in stead of knowing source number or SNR. On simulated and wind tunnel data, proposed approach is compared with the beamforming, DAMAS, Diagonal Remove DAMAS, Robust DAMAS with sparsity constraint (SC-RDAMAS), Covariance Matrix Fitting (CMF) and CLEAN.

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تاریخ انتشار 2012