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A Sparse Conjugate Gradient Adaptive Filter.

Authors
  • Lee, Ching-Hua1
  • Rao, Bhaskar D1
  • Garudadri, Harinath1
  • 1 Department of Electrical and Computer Engineering, University of California, San Diego, CA 92093 USA.
Type
Published Article
Journal
IEEE signal processing letters
Publication Date
Jan 01, 2020
Volume
27
Pages
1000–1004
Identifiers
DOI: 10.1109/LSP.2020.3000459
PMID: 32742159
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

In this letter, we propose a novel conjugate gradient (CG) adaptive filtering algorithm for online estimation of system responses that admit sparsity. Specifically, the Sparsity-promoting Conjugate Gradient (SCG) algorithm is developed based on iterative reweighting methods popular in the sparse signal recovery area. We propose an affine scaling transformation strategy within the reweighting framework, leading to an algorithm that allows the usage of a zero sparsity regularization coefficient. This enables SCG to leverage the sparsity of the system response if it already exists, while not compromising the optimization process. Simulation results show that SCG demonstrates improved convergence and steady-state properties over existing methods.

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