Name
Title | ||
|---|---|---|
Iteratively Re-Weighted Least Squares For Sparse Signal Reconstruction From Noisy Measurements |
Abstract | ||
|---|---|---|
Finding sparse solutions of under-determined systems of linear equations is a problem of significance importance in signal processing and statistics. In this paper we study an iterative reweighted least squares (IRLS) approach to find sparse solutions of underdetermined system of equations based on smooth approximation of the L-0 norm and the method is extended to find sparse solutions from noisy measurements. Analysis of the proposed methods show that weaker conditions on the sensing matrices are required. Simulation results demonstrate that the proposed method requires fewer samples than existing methods, while maintaining a reconstruction error of the same order and demanding less computational complexity. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CISS.2009.5054762 | 2009 43RD ANNUAL CONFERENCE ON INFORMATION SCIENCES AND SYSTEMS, VOLS 1 AND 2 |
Keywords | Field | DocType |
Compressed sensing, sampling methods, signal reconstruction, underdetermined systems of linear equations, reweighted least squares | Least squares,Mathematical optimization,Underdetermined system,Iterative method,Computer science,Sparse approximation,Iteratively reweighted least squares,Compressed sensing,Signal reconstruction,Computational complexity theory | Conference |
Citations | PageRank | References |
12 | 1.03 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafael E. Carrillo | 1 | 250 | 15.90 |
| Kenneth E. Barner | 2 | 812 | 70.19 |