Asymptotic performance analysis of compressed sensing reconstruction algorithm

• Main Author: Ong, Yan Lin
• Other Authors: Anamitra Makur
• Format: Final Year Project
• Language: English
• Published: 2019
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: http://hdl.handle.net/10356/78370
• Institution: Nanyang Technological University

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.