Subband Adaptive Filtering with l1-Norm Constraint for Sparse System Identification
Author
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-12
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
This paper presents a new approach of the normalized subband adaptive filter (NSAF) which directly exploits the sparsity condition of an underlying system for sparse system identification.
The proposed NSAF integrates a weighted l1-norm constraint into the cost function of the NSAF algorithm.
To get the optimum solution of the weighted l1-norm regularized cost function, a subgradient calculus is employed, resulting in a stochastic gradient based update recursion of the weighted l1-norm regularized NSAF.
The choice of distinct weighted l1-norm regularization leads to two versions of the l1-norm regularized NSAF.
Numerical results clearly indicate the superior convergence of the l1-norm regularized NSAFs over the classical NSAF especially when identifying a sparse system.
American Psychological Association (APA)
Choi, Young-Seok. 2013. Subband Adaptive Filtering with l1-Norm Constraint for Sparse System Identification. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-1010038
Modern Language Association (MLA)
Choi, Young-Seok. Subband Adaptive Filtering with l1-Norm Constraint for Sparse System Identification. Mathematical Problems in Engineering No. 2013 (2013), pp.1-7.
https://search.emarefa.net/detail/BIM-1010038
American Medical Association (AMA)
Choi, Young-Seok. Subband Adaptive Filtering with l1-Norm Constraint for Sparse System Identification. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-1010038
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1010038