Sparse Deconvolution for Moving-Source Localization

Mai Quyen Pham (1) , Benoit Oudompheng (1, 2) , Barbara Nicolas (3) , Jerome I. Mars (1)

1 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
2 MicrodB
3 Imagerie Ultrasonore

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.

Mots clés: Smoothed L1/L2 regularization; Sparse representation; Moving-source localization; Beamforming deconvolution; Acoustic signal processing

Domaines: Traitement du signal et de l'image [eess.SP]

Mai Quyen Pham, Benoit Oudompheng, Barbara Nicolas, Jerome I. Mars. Sparse Deconvolution for Moving-Source Localization. ICASSP 2016 - 41st IEEE International Conference on Acoustics, Speech and Signal Processing, Mar 2016, Shanghai, China. pp.355-359, ⟨10.1109/ICASSP.2016.7471696⟩. ⟨hal-01343760⟩