DocumentCode :
1890961
Title :
Iteratively re-weighted least squares for sparse signal reconstruction from noisy measurements
Author :
Carrillo, Rafael E. ; Barner, Kenneth E.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Delaware, Newark, DE
fYear :
2009
fDate :
18-20 March 2009
Firstpage :
448
Lastpage :
453
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 L0 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.
Keywords :
iterative methods; least squares approximations; signal reconstruction; statistical analysis; iteratively re-weighted least squares approximation; linear equations; reconstruction error; signal processing; sparse signal reconstruction; statistical analysis; Computational complexity; Computational modeling; Equations; Iterative methods; Least squares approximation; Least squares methods; Signal processing; Signal reconstruction; Sparse matrices; Statistics; Compressed sensing; re-weighted least squares; sampling methods; signal reconstruction; underdetermined systems of linear equations;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Sciences and Systems, 2009. CISS 2009. 43rd Annual Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
978-1-4244-2733-8
Electronic_ISBN :
978-1-4244-2734-5
Type :
conf
DOI :
10.1109/CISS.2009.5054762
Filename :
5054762
Link To Document :
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