I. Introduction
Linear sampling of sparse signals, with a number of samples close to their sparsity level, has recently received great attention under the name of Compressed Sensing or Compressive Sampling (CS) [1], [2]. A -sparse signal is defined as a signal with nonzero expansion coefficients in some orthonormal basis or frame. The goal of compressed sensing is to find measurement matrices , followed by reconstruction algorithms which allow robust recovery of sparse signals using the least number of measurements , and low computational complexity.