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Comparison of some commonly used algorithms for sparse signal reconstruction
Due to excessive need for faster propagations of signals and necessity to
reduce number of measurements and rapidly increase efficiency, new sensing
theories have been proposed. Conventional sampling approaches that follow
Shannon-Nyquist theorem require the sampling rate to be at least twice the
maximum frequency of the signal. This has triggered scientists to examine the
possibilities of creating a new path for recovering signals using much less
samples and therefore speeding up the process and satisfying the need for
faster realization. As a result the compressive sensing approach has emerged.
This breakthrough makes signal processing and reconstruction much easier, not
to mention that is has a vast variety of applications. In this paper some of
the commonly used algorithms for sparse signal recovery are compared. The
reconstruction accuracy, mean squared error and the execution time are
compared.Comment: submitted to The 8th Mediterranean Conference on Embedded Computing -
MECO'201