  Intensively growing approach in signal processing and acquisition, the
Compressive Sensing approach, allows sparse signals to be recovered from small
number of randomly acquired signal coefficients. This paper analyses some of
the commonly used threshold-based algorithms for sparse signal reconstruction.
Signals satisfy the conditions required by the Compressive Sensing theory. The
Orthogonal Matching Pursuit, Iterative Hard Thresholding and Single Iteration
Reconstruction algorithms are observed. Comparison in terms of reconstruction
error and execution time is performed within the experimental part of the
paper.
