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.
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https://hal-supelec.archives-ouvertes.fr/hal-00658941
Contributor : Karine El Rassi <>
Submitted on : Wednesday, January 11, 2012 - 3:57:55 PM
Last modification on : Friday, May 3, 2019 - 3:52:05 PM

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Ning Chu, José Picheral, Ali Mohammad-Djafari. A robust super-resolution approach for near-field wideband acoustic imaging with sparsity constraint. 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2011) , Dec 2011, Bilbao, Spain. 6 p., ⟨10.1109/ISSPIT.2011.6151579⟩. ⟨hal-00658941⟩

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