Sparse deconvolution for moving-source localization
Mai Quyen Pham, Benoit Oudompheng, Barbara Nicolas, Jérôme Igor Mars · 2016
In this paper, we propose a method for moving-source localization based on beamforming output and on sparse representation of the source positions. The goal of this method is to achieve spatial deconvolution of the beamforming, to provide accurate source localization for pass-by experiments. To perform this deconvolution, we use a smooth approximation of ℓ1/ℓ2[1], which is well suited for the recovery of sparse signals. We validate this method on simulated data, and compare it to the DAMAS-MS method [2], one of the classical methods used in beamforming deconvolution.