NonConvex fterativefy Reweighted Least Square Optimization in Compressive Sensing

摘要：

In this paper, we study a method for sparse signal recovery with the help of iteratively reweighted least square approach, which in many situations outperforms other reconstruction method mentioned in literature in a way that comparatively fewer measurements are needed for exact recovery.The algorithm given involves solving a sequence of weighted minimization for nonconvex problems where the weights for the next iteration are determined from the value of current solution.We present a number of experiments demonstrating the performance of the algorithm.The performance of the algorithm is studied via computer simulation for different number of measurements, and degree of sparsity.Also the simulation results show that improvement is achieved by incorporating regularization strategy.

关键词： Compressive sensing regular/zation optimization

作者: Madhuparna Chakraborty Alaka Bank Ravinder Nath Victor Dutta

作者单位: Asst Prof.,EEE Dept,S.M.I.T,S/kkim Manipa/ University,Sikkim,India Asst Prof.,ECE Dept.,I.T.E.R,Siksha O Anusandhan University,Bhubaneswar,Orissa,India Associate Prof.,EE Dept.,NIT Hamirpur,Hamirpur,Himacrial Pradesh,India Asst Prof.,EEE Dept.,S.M.I.T,Sikkim Manipal University,Sikkim,India

会议类型: 国际会议

会议名称: 2011 2nd International Conference on Material and Manufacturing Technology(2011第二届材料与制造技术国际会议 ICMMT2011)

会议地点: 厦门

会议语种:英文

页码: 629-633

在线出版日期: 2011-07-08（万方平台首次上网日期，不代表论文的发表时间）