Sparse deconvolution for moving-source localization

Pham, Mai Quyen; Oudompheng, Benoit; Nicolas, Barbara; Mars, Jérôme

doi:10.1109/icassp.2016.7471696

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Inverse Problem Formulation 221

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“…1, right), which are 42 the unknowns to be estimated. This expression is 43 frequently used in deconvolution [20,27,31,41], al-…”

Section: Inverse Problem Formulation 22mentioning

confidence: 99%

A Noise-Robust Method with Smoothedℓ1/ℓ2Regularization for Sparse Moving-Source Mapping

et al. 2017

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The method described here performs blind deconvolution of the beamforming output in the frequency domain. To provide accurate blind deconvolution, sparsity priors are introduced with a smoothed 1 / 2 regularization term. As the mean of the noise in the power spectrum domain depends on its variance in the time domain, the proposed method includes a variance estimation step, which allows more robust blind deconvolution. Validation of the method on both simulated and real data, and of its performance, are compared with two well-known methods from the literature: the deconvolution approach for the mapping of acoustic sources, and sound density modeling.

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“…1, right), which are 42 the unknowns to be estimated. This expression is 43 frequently used in deconvolution [20,27,31,41], al-…”

Section: Inverse Problem Formulation 22mentioning

confidence: 99%