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Due to excessive need for faster propagations of signals and necessity toreduce number of measurements and rapidly increase efficiency, new sensingtheories have been proposed. Conventional sampling approaches that followShannon-Nyquist theorem require the sampling rate to be at least twice themaximum frequency of the signal. This has triggered scientists to examine thepossibilities of creating a new path for recovering signals using much lesssamples and therefore speeding up the process and satisfying the need forfaster realization. As a result the compressive sensing approach has emerged.This breakthrough makes signal processing and reconstruction much easier, notto mention that is has a vast variety of applications. In this paper some ofthe commonly used algorithms for sparse signal recovery are compared. Thereconstruction accuracy, mean squared error and the execution time arecompared.

Comparison of some commonly used algorithms for sparse signal reconstruction