A weighted l1-minimization for distributed compressive sensing

TitleA weighted l1-minimization for distributed compressive sensing
Publication TypeThesis
Year of Publication2015
AuthorsXiaowei Li
Month09
UniversityThe University of British Columbia
CityVancouver
Thesis Typemasters
Keywordsdistributed compressive sensing, MSc, thesis, weighted $\ell_1$
Abstract

Distributed Compressive Sensing (DCS) studies the recovery of jointly sparse signals. Compared to separate recovery, the joint recovery algorithms in DCS are usually more effective as they make use of the joint sparsity. In this thesis, we study a weighted l1-minimization algorithm for the joint sparsity model JSM-1 proposed by Baron et al. Our analysis gives a sufficient null space property for the joint sparse recovery. Furthermore, this property can be extended to stable and robust settings. We also presents some numerical experiments for this algorithm.

Notes

(MSc)

URLhttps://www.slim.eos.ubc.ca/Publications/Public/Thesis/2015/li2015THwmd/li2015THwmd.pdf
Citation Keyli2015THwmd