Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/78370
Title: Asymptotic performance analysis of compressed sensing reconstruction algorithm
Authors: Ong, Yan Lin
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2019
Abstract: 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.
URI: http://hdl.handle.net/10356/78370
Schools: School of Electrical and Electronic Engineering 
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP Report.pdf
  Restricted Access
1.41 MBAdobe PDFView/Open

Page view(s)

159
Updated on Jul 15, 2024

Download(s)

3
Updated on Jul 15, 2024

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.