Abstract : We consider the problem of calibrating a compressed sensing measurement system under the assumption that the decalibration consists of unknown complex gains on each measure. We focus on {\em blind} calibration, using measures performed on a few unknown (but sparse) signals. In the considered context, we study several sub-problems and show that they can be formulated as convex optimization problems, which can be solved easily using off-the-shelf algorithms. Numerical simulations demonstrate the effectiveness of the approach even for highly uncalibrated measures, when a sufficient number of (unknown, but sparse) calibrating signals is provided.
Type de document :
Document associé à des manifestations scientifiques
international biomedical and astronomical signal processing (BASP) Frontiers workshop, Jan 2013, Villars-sur-Ollon, Switzerland
https://hal.inria.fr/hal-00751360
Contributeur : Cagdas Bilen
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Soumis le : mardi 13 novembre 2012 - 11:46:41
Dernière modification le : jeudi 21 mars 2019 - 14:44:51
Document(s) archivé(s) le : jeudi 14 février 2013 - 03:41:12
Cagdas Bilen, Gilles Puy, Rémi Gribonval, Laurent Daudet. Blind Sensor Calibration in Sparse Recovery. international biomedical and astronomical signal processing (BASP) Frontiers workshop, Jan 2013, Villars-sur-Ollon, Switzerland. 〈hal-00751360〉
