A New and Robust AMP Algorithm for Non IID Matrices Based on Bayesian Theory in Compressed Sensing

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Ansari Ram F.; KHADEMI M.; EBRAHIMI MOGHADDAM A.; SADOGHI YAZDI H.

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR Year:2020 | Volume:18 | Issue:1 Page(s): 15-28

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

614

Download:

Cites:

Information Journal Paper

Title

A New and Robust AMP Algorithm for Non IID Matrices Based on Bayesian Theory in Compressed Sensing

Author(s)

Ansari Ram F. | KHADEMI M. | EBRAHIMI MOGHADDAM A. | SADOGHI YAZDI H. | Issue Writer Certificate

Keywords

Approximate message passingQ1

compressed sensingQ1

IID Gaussian matricesQ1

low-rank product matricesQ1

row orthogonal matricesQ1

Abstract

AMP is a low-cost iterative algorithm for recovering signal in compressed sensing. When the sampling matrix has IID zero-mean Gaussian elements, the convergence of AMP is analytically guaranteed. But for other sampling matrices, especially ill-conditioned matrices, the recovery performance of AMP degrades and even may be diverged. This problem limits the use of AMP in some applications such as imaging. In this paper, a method is proposed for modifying the AMP algorithm based on Bayesian theory for non-IID matrices. Simulation results show better robustness properties of the proposed algorithm for non-IID matrices in comparison with previous works. In other words, the proposed method has more precision in recovery, and converges with less iterations.

Cites

No record.

References

No record.

Cite

APA: Copy

Ansari Ram, F., KHADEMI, M., EBRAHIMI MOGHADDAM, A., & SADOGHI YAZDI, H.. (2020). A New and Robust AMP Algorithm for Non IID Matrices Based on Bayesian Theory in Compressed Sensing. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, 18(1 ), 15-28. SID. https://sid.ir/paper/228426/en

Vancouver: Copy

Ansari Ram F., KHADEMI M., EBRAHIMI MOGHADDAM A., SADOGHI YAZDI H.. A New and Robust AMP Algorithm for Non IID Matrices Based on Bayesian Theory in Compressed Sensing. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR[Internet]. 2020;18(1 ):15-28. Available from: https://sid.ir/paper/228426/en

IEEE: Copy

F. Ansari Ram, M. KHADEMI, A. EBRAHIMI MOGHADDAM, and H. SADOGHI YAZDI, “A New and Robust AMP Algorithm for Non IID Matrices Based on Bayesian Theory in Compressed Sensing,” NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, vol. 18, no. 1 , pp. 15–28, 2020, [Online]. Available: https://sid.ir/paper/228426/en

Related Journal Papers