Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is very low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at high sparsity. © 1991-2012 IEEE.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Manuscript received April 24, 2012; revised July 23, 2012; accepted July 30, 2012. Date of publication August 23, 2012; date of current version November 20, 2012. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Z. Jane Wang. This work was partially supported by SABIC through an internally funded project from DSR, KFUPM (Project No. SB101006) and partially by King Abdulaziz City for Science and Technology (KACST) through the Science & Technology Unit at KFUPM (Project No. 09-ELE763-04) as part of the National Science, Technology and Innovation Plan. The work of T. Y. Al-Naffouri was also supported by the Fullbright Scholar Program.
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering