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.


Presented at:
International biomedical and astronomical signal processing (BASP) Frontiers workshop, January 2013
Year:
2013
Laboratories:




 Record created 2013-09-20, last modified 2018-03-17

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