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Author Gribonval, R. ♦ Chardon, G. ♦ Daudet, L.
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Description We consider the problem of calibrating a compressed sensing mea-surement system under the assumption that the decalibration consists in unknown gains on each measure. We focus on blind calibration, us-ing measures performed on a few unknown (but sparse) signals. A naive formulation of this blind calibration problem, using `1 minimization, is reminiscent of blind source separation and dictionary learning, which are known to be highly non-convex and riddled with local minima. In the considered context, we show that in fact this formulation can be exactly expressed as a convex optimization problem, and can be solved 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. We ob-serve that the success/failure of the approach seems to obey sharp phase transitions. 1
in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article