Teke, OğuzhanGürbüz, A.C.Arıkan, Orhan2016-02-082016-02-082012http://hdl.handle.net/11693/28196Date of Conference: 18-20 April 2012Compressive Sensing (CS) theory details how a sparsely represented signal in a known basis can be reconstructed using less number of measurements. However in reality there is a mismatch between the assumed and the actual bases due to several reasons like discritization of the parameter space or model errors. Due to this mismatch, a sparse signal in the actual basis is definitely not sparse in the assumed basis and current sparse reconstruction algorithms suffer performance degradation. This paper presents a novel orthogonal matching pursuit algorithm that has a controlled perturbation mechanism on the basis vectors, decreasing the residual norm at each iteration. Superior performance of the proposed technique is shown in detailed simulations. © 2012 IEEE.TurkishBasis vectorCompressive sensingControlled perturbationModel errorsOrthogonal matching pursuitParameter spacesPerformance degradationResidual normSparse reconstructionSparse recoverySparse signalsChannel estimationDegradationSignal processingConference Paper10.1109/SIU.2012.6204606