# A weighted l1-minimization for distributed compressive sensing

 Title A weighted l1-minimization for distributed compressive sensing Publication Type Thesis Year of Publication 2015 Authors Xiaowei Li Month 09 University The University of British Columbia City Vancouver Thesis Type masters Keywords distributed 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) URL https://www.slim.eos.ubc.ca/Publications/Public/Thesis/2015/li2015THwmd/li2015THwmd.pdf Citation Key li2015THwmd