Please use this identifier to cite or link to this item:
Title: Compressive sensing reconstruction algorithms using partially correct signal information
Authors: Sekar Sathiya Narayanan
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2017
Source: Sekar Sathiya Narayanan. (2017). Compressive sensing reconstruction algorithms using partially correct signal information. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Compressive Sensing (CS) ensures the reconstruction of a sparse signal from a set of linear measurements that are fewer compared to the signal length. The sparse signal can be reconstructed using a convex relaxation algorithm such as Basis Pursuit (BP) or a Greedy Pursuit (GP) such as Backtracking Matching Pursuit (BMP). If some information regarding the signal support (non-zero locations) is available in the form of Partially Known Support (PKS), the same sparse signal can be recovered with higher accuracy. However, the size and accuracy of the PKS varies depending upon the signal model and characteristics. A generic PKS based reconstruction algorithm might work well in a particular scenario but fail in another. This thesis focuses on developing PKS based reconstruction algorithms for different scenarios wherein they make effective use of the available PKS.
DOI: 10.32657/10356/69572
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
Thesis_G1101654H_EEE.pdf1.62 MBAdobe PDFThumbnail

Page view(s)

Updated on Jul 19, 2024

Download(s) 50

Updated on Jul 19, 2024

Google ScholarTM




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