A robust super-resolution approach with sparsity constraint in acoustic imaging

Ning Chu (1) , José Picheral (2) , Ali Mohammad-Djafari (1) , Gac Nicolas (1)

1 L2S - Laboratoire des signaux et systèmes
2 E3S - Supélec Sciences des Systèmes

Acoustic imaging is a standard technique for mapping acoustic source powers and positions from limited observations on microphone sensors, which often causes an ill-conditioned inverse problem. In this article, we firstly improve the forward model of acoustic power propagation by considering background noises at the sensor array, and the propagation uncertainty caused by wind tunnel effects. We then propose a robust super-resolution approach via sparsity constraint for acoustic imaging in strong background noises. The sparsity parameter is adaptively derived from the sparse distribution of source powers. The proposed approach can jointly reconstruct source powers and positions, as well as the background noise power. Our approach is compared with the conventional beamforming, deconvolution and sparse regularization methods by simulated, wind tunnel data and hybrid data respectively. It is feasible to apply the proposed approach for effectively mapping monopole sources in wind tunnel tests.

Keywords: Localization; parameter estimation; acoustic imaging; sparsity constraint; robust super-resolution

Domains: Signal and Image Processing; Signal and Image processing

Ning Chu, José Picheral, Ali Mohammad-Djafari, Gac Nicolas. A robust super-resolution approach with sparsity constraint in acoustic imaging. Applied Acoustics, 2013. ⟨hal-00794236v7⟩