000188608 001__ 188608
000188608 005__ 20190316235710.0
000188608 037__ $$aCONF
000188608 245__ $$aBlind Sensor Calibration in Sparse Recovery
000188608 269__ $$a2013
000188608 260__ $$c2013
000188608 336__ $$aConference Papers
000188608 520__ $$aWe 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.
000188608 700__ $$aBilen, Cagdas
000188608 700__ $$0242927$$g179918$$aPuy, Gilles
000188608 700__ $$aGribonval, Rémi
000188608 700__ $$aDaudet, Laurent
000188608 7112_ $$dJanuary 2013$$aInternational biomedical and astronomical signal processing (BASP) Frontiers workshop
000188608 8564_ $$uhttp://hal.inria.fr/hal-00751360$$zURL
000188608 909C0 $$xU10380$$0252392$$pLTS2
000188608 909CO $$qGLOBAL_SET$$pconf$$ooai:infoscience.tind.io:188608$$pSTI
000188608 917Z8 $$x179918
000188608 917Z8 $$x179918
000188608 937__ $$aEPFL-CONF-188608
000188608 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000188608 980__ $$aCONF