Comparison of some commonly used algorithms for sparse signal reconstruction

TL;DR

This paper compares various algorithms for sparse signal reconstruction, evaluating their accuracy, error, and speed to identify the most effective methods in the context of compressive sensing.

Contribution

It provides a comparative analysis of popular sparse signal recovery algorithms, highlighting their performance differences in accuracy and computational efficiency.

Findings

Certain algorithms outperform others in reconstruction accuracy.

Trade-offs exist between speed and precision among the algorithms.

The study guides selecting suitable algorithms for specific applications.

Abstract

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.