Blind Sensor Calibration in Sparse Recovery

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.
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https://hal.inria.fr/hal-00751360
Contributor : Cagdas Bilen <>
Submitted on : Tuesday, November 13, 2012 - 11:46:41 AM
Last modification on : Thursday, July 4, 2019 - 11:00:07 AM
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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⟩

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