AN IMPROVED GREEDY ALGORITHM FOR SIGNAL RECOVERY FROM RANDOM MEASUREMENTS

摘要：

In this paper a novel greedy algorithm, called least squares orthogonal pursuit (LSOP), is proposed to solve the sparse approximation problem in practical compressive sensing. Unlike orthogonal matching pursuit (OMP) which finds the support set of the unknown signal iteratively with the idea of matching filter, LSOP selects the candidate according to the least squares solution of the residual signal. It is shown that the proposed algorithm surpasses OMP because its coherence statistic is smaller than that of the latter. In addition, the idea that the least squares solution aids support set selection can be extended to all OMP-based algorithms and improves their performances. Several numerical experiments demonstrate that the proposed LSOP family has better behaviors in solving sparse recovery problem than the available OMP family.

关键词： adaptive filter 10-LMS mean square performance uncorrelated input

作者: Jian Jin Yuantao Gu Shunliang Mei

作者单位: Department of Electronic Engineering, Tsinghua University,Beijing 100084, CHINA

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 82-85

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）