Blind calibration for compressed sensing by convex optimization

Mathematics – Statistics Theory

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

We consider the problem of calibrating a compressed sensing measurement system under the assumption that the decalibration consists in unknown gains on each measure. We focus on {\em blind} calibration, using measures performed on a few unknown (but sparse) signals. A naive formulation of this blind calibration problem, using $\ell_{1}$ 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 observe that the success/failure of the approach seems to obey sharp phase transitions.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Blind calibration for compressed sensing by convex optimization does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Blind calibration for compressed sensing by convex optimization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Blind calibration for compressed sensing by convex optimization will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-9153

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.