000188610 001__ 188610
000188610 005__ 20190316235710.0
000188610 022__ $$a1053-587X
000188610 02470 $$2ISI$$a000340847000017
000188610 0247_ $$2doi$$a10.1109/TSP.2014.2342651
000188610 037__ $$aARTICLE
000188610 245__ $$aConvex Optimization Approaches for Blind Sensor Calibration using Sparsity
000188610 269__ $$a2014
000188610 260__ $$bInstitute of Electrical and Electronics Engineers$$c2014
000188610 336__ $$aJournal Articles
000188610 520__ $$aWe investigate a compressive sensing framework in which the sensors introduce a distortion to the measurements in the form of unknown gains. We focus on blind calibration, using measures performed on multiple unknown (but sparse) signals and formulate the joint recovery of the gains and the sparse signals as a convex optimization problem. The first proposed approach is an extension to the basis pursuit optimization which can estimate the unknown gains along with the unknown sparse signals. Demonstrating that this approach is successful for a sufficient number of input signals except in cases where the phase shifts among the unknown gains varies significantly, a second approach is proposed that makes use of quadratic basis pursuit optimization to calibrate for constant amplitude gains with maximum variance in the phases. An alternative form of this approach is also formulated to reduce the complexity and memory requirements and provide scalability with respect to the number of input signals. Finally a third approach is formulated which combines the first two approaches for calibration of systems with any variation in the gains. The performance of the proposed algorithms are investigated extensively through numerical simulations, which demonstrate that simultaneous signal recovery and calibration is possible when sufficiently many (unknown, but sparse) calibrating signals are provided.
000188610 6531_ $$aCompressed sensing
000188610 6531_ $$aBlind calibration
000188610 6531_ $$aPhase estimation
000188610 6531_ $$aPhase retrieval
000188610 6531_ $$aLifting
000188610 700__ $$aBilen, Cagdas
000188610 700__ $$0242927$$g179918$$aPuy, Gilles
000188610 700__ $$aGribonval, Rémi
000188610 700__ $$aDaudet, Laurent
000188610 773__ $$j62$$tIEEE Transactions on Signal Processing$$k18$$q4847-4856
000188610 8564_ $$uhttp://hal.inria.fr/hal-00853225$$zURL
000188610 909C0 $$xU10380$$0252392$$pLTS2
000188610 909CO $$qGLOBAL_SET$$pSTI$$ooai:infoscience.tind.io:188610$$particle
000188610 917Z8 $$x179918
000188610 917Z8 $$x179918
000188610 917Z8 $$x148230
000188610 937__ $$aEPFL-ARTICLE-188610
000188610 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000188610 980__ $$aARTICLE