Subband adaptive filtering with \(l_1\)-norm constraint for sparse system identification
type: Item
instance of: scholarly article
title: Subband adaptive filtering with \(l_1\)-norm constraint for sparse system identification
zbMATH Open document ID: 1299.94042
DOI: 10.1155/2013/601623
author: Young-Seok Choi
published in: Mathematical Problems in Engineering
publication date: 2014-11-24T00:00:00Z
review text: Summary: 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 \(l_1\)-norm constraint into the cost function of the NSAF algorithm. To get the optimum solution of the weighted \(l_1\)-norm regularized cost function, a subgradient calculus is employed, resulting in a stochastic gradient based update recursion of the weighted \(l_1\)-norm regularized NSAF. The choice of distinct weighted \(l_1\)-norm regularization leads to two versions of the \(l_1\)-norm regularized NSAF. Numerical results clearly indicate the superior convergence of the \(l_1\)-norm regularized NSAFs over the classical NSAF especially when identifying a sparse system.
Mathematics Subject Classification ID: 94A12
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 93E12
zbMATH DE Number: 6372817
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/601623
OpenAlex ID: W2140546810
Wikidata QID: Q59027187
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description: scientific article; zbMATH DE number 6372817