Blind Sensor Calibration in Sparse Recovery

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 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.


Présenté à:
International biomedical and astronomical signal processing (BASP) Frontiers workshop, January 2013
Année
2013
Laboratoires:




 Notice créée le 2013-09-20, modifiée le 2019-03-16

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