A robust super-resolution approach for near-field wideband acoustic imaging with sparsity constraint
Abstract
Acoustic source imaging has nowadays been widely used in source localization and separation. In this paper, based on the deconvolution methods (DAMAS), we propose a robust super-resolution approach with sparsity constraint (SC-RDAMAS) to estimate both the positions and powers of the sources, as well as the noise variance in low Signal to Noise Ratio (SNR) situation. For effectively applying sparsity constraint, we explore a better initialization of source number to determine the bound of total source powers. By simulations and real data, we show that our SC-RDAMAS can obtain more accurate estimations of source positions and averaging powers, and can be more robust to strong noise interference, by comparison with the state of the art methods: the Beamforming, DAMAS, DAMAS with sparsity constraint (SC-DAMAS) and the Covariance Matrix Fitting (CMF) method. Indeed the computation burden of the proposed method is much lower than the CMF, so that our SC-RDAMAS is more applicable to scan the large region with super resolutions.