On the Performance Analysis of Normalized Subband Adaptive Filtering Algorithm with Sparse Subfilters

Subband adaptive filtering algorithms can increase the convergence rate of system identification tasks when the input signal is colored. Recently, a new normalized subband adaptive filtering algorithm with sparse subfilters (NSAF-SF) has been proposed, whose main advantage is the lower computational...

Full description

Saved in:
Bibliographic Details
Published inCircuits, systems, and signal processing Vol. 39; no. 11; pp. 5830 - 5847
Main Authors Xavier, Pedro P. S., Haddad, Diego B., Petraglia, Mariane R.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.11.2020
Springer Nature B.V
Subjects
Adaptive algorithms
Adaptive filters
Adaptive systems
Algorithms
Circuits and Systems
Computer simulation
Convergence
Electrical Engineering
Electronics and Microelectronics
Engineering
Instrumentation
Signal,Image and Speech Processing
Steady state
System identification
Transfer functions
Sparsity-aware adaptive filtering
Multirate signal processing
Stochastic models
Subband adaptive filtering
Online AccessGet full text

Cover

More Information
Summary:Subband adaptive filtering algorithms can increase the convergence rate of system identification tasks when the input signal is colored. Recently, a new normalized subband adaptive filtering algorithm with sparse subfilters (NSAF-SF) has been proposed, whose main advantage is the lower computational complexity when compared to state-of-the-art subband approaches, while maintaining similar convergence performance. In this paper, the first- and second-order stochastic analyses of the NSAF-SF algorithm are presented in order to provide predictions about its transient and steady-state performances. A relationship between the adaptive subband coefficients and the ideal fullband transfer function is derived, and the algorithm is proven to produce an asymptotically unbiased solution. In addition, a closed-form expression is obtained for the steady-state mean square deviation (MSD) of the subfilter coefficients. Although the proposed analyses use conventional assumptions of statistical independence, they do not assume a specific stochastic characteristic for the input signal (e.g., Gaussianity or whiteness). Transient and steady-state theoretical predictions of the MSD are confirmed by simulations.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-020-01438-2