Abstract
Compressive sensing (CS)is a novel sampling method that samples signals efficiently than sub-Nyquist rate. CS has recently gained a lot of attention due to its exploitation of signal sparsity. Sparsity, an inherent characteristic of many natural signals, enables the signal to be stored in few samples and subsequently be recovered accurately. In this paper the focus is on estimating a proper measurement matrix for compressive sampling of signals. The performance parameters like Mean Square Error (MSE), Signal to Noise Ratio (SNR), Perceptual Evaluation Speech Quality (PESQ) are measured for various reconstruction algorithms like L1 Minimization, Compressive Sampling Matching Pursuit (CoSaMP), Orthogonal Matching Pursuit (OMP). It is observed that OMP gives better results when compared to L1 minimization and CoSaMP.
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