Compressed Sensing and Reconstruction with Bernoulli Matrices

摘要：

Compressed sensing seeks to recover a sparse or compressible signal from a small number of linear and nonadaptive measurements. While most of the studies so far focus on the prominent Gaussian random measurements, we investigate the performances of matrices with Bernoulli distribution. As extensions of symmetric signs ensemble, random binary ensemble and semi-Hadamard ensemble are proposed as sensing matrices with simplex structures. Based on some results of symmetric signs ensemble and the concept of compressed sensing matrices, we obtain a theoretical result that signal compressed sensing using random binary matrices can be exactly reconstructed with high probability. In reconstruction processes, the fast and lowconsumed orthogonal matching pursuit is adopted. Numerical results show that such matrices perform equally well to the Gaussian matrices.

关键词： compressed sensing Bernoulli matrices random binary ensemble semi-Hadamard ensemble orthogonal matching pursuit

作者: Gesen Zhang Shuhong Jiao Xiaoli Xu Lan Wang

作者单位: Information and Communication Engineering College Harbin Engineering University Harbin,Heilongjiang Department of Mathematics Beijing Institute of Technology Beijing,China

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-6

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）