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A Generalized Proportionate-Type Normalized Subband Adaptive Filter

Authors
  • Chen, Kuan-Lin
  • Lee, Ching-Hua
  • Rao, Bhaskar D.
  • Garudadri, Harinath
Type
Published Article
Journal
Conference record. Asilomar Conference on Signals, Systems & Computers
Publication Date
Nov 01, 2019
Volume
2019
Pages
749–753
Identifiers
DOI: 10.1109/ieeeconf44664.2019.9048906
PMID: 35264844
PMCID: PMC8903025
Source
PubMed Central
Keywords
Disciplines
  • Article
License
Unknown

Abstract

We show that a new design criterion, i.e., the least squares on subband errors regularized by a weighted norm, can be used to generalize the proportionate-type normalized subband adaptive filtering (PtNSAF) framework. The new criterion directly penalizes subband errors and includes a sparsity penalty term which is minimized using the damped regularized Newton’s method. The impact of the proposed generalized PtNSAF (GPtNSAF) is studied for the system identification problem via computer simulations. Specifically, we study the effects of using different numbers of subbands and various sparsity penalty terms for quasi-sparse, sparse, and dispersive systems. The results show that the benefit of increasing the number of subbands is larger than promoting sparsity of the estimated filter coefficients when the target system is quasi-sparse or dispersive. On the other hand, for sparse target systems, promoting sparsity becomes more important. More importantly, the two aspects provide complementary and additive benefits to the GPtNSAF for speeding up convergence.

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