期刊名称：International Journal of Electronics and Computer Science Engineering
电子版ISSN：2277-1956
出版年度：2012
卷号：1
期号：3
页码：886-891
出版社：Buldanshahr : IJECSE
摘要：Compressive sensing is a new signal acquition technology with the potential to reduce the number of measurements requires acquiring signals that are sparse or compressible in some basis. Rather than uniform sampling the signal, compressive sensing computes inner product relative to some basis vectors in which the data vectors need to be sparse. The signal is then recovered by a optimization routine that ensure the recovered signal is both consistent with the measurements and sparse. In this paper, we studied the Iterative Reweighted Least Square (IRLS) algorithm for nonconvex problem using minimization, for recovering the original sparse signal, from very few measurements. The performance of the algorithm is studied via computer simulation for different SNRs ranges, number of measurements, degree of sparsity, and various values of . Simulation results show that the performance of the algorithm improves by incorporating regularization strategy
