Comparison of threshold-based algorithms for sparse signal recovery
This work provides a comparative analysis for researchers in signal processing, but it is incremental as it focuses on existing algorithms without introducing new methods.
The paper compares threshold-based algorithms for sparse signal recovery under compressive sensing conditions, evaluating Orthogonal Matching Pursuit, Iterative Hard Thresholding, and Single Iteration Reconstruction in terms of reconstruction error and execution time.
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.