Asymptotic performance analysis of compressed sensing reconstruction algorithm - Ong, Yan Lin (2019)
The theory and applications on Compressed Sensing is a promising, quickly developing area which garnered a great amount of interest in the field of engineering, mathematics, analytics and info-communication. CS introduces a skeleton/template which allows for the concurrently execution of recovering and compressing of vectors in a bounded dimension. It deals with the recovery of sparse high-dimensional input signals with a considerably small amount of sample measurements through the execution of some efficient algorithms. Quite a few algorithms have been developed for the purpose of signal reconstruction from compressed measurements, and especially enticing amongst them is greedy pursuit algorithm: Orthogonal Matching Pursuit (OMP). This paper investigates how the performance of OMP changes when the various parameter such as linear dimension n, number of measurements m and sparsity are increased.