A unified approach to steady-state performance analysis of adaptive filters without using the independence assumptions

Authors:
Mohammad Shams Esfand Abadi;Ali Mahlooji Far
Affiliations:
Electrical and Computer Engineering Department, Tarbiat Modarres University, P.O. Box 14115-143, Tehran, Iran;Electrical and Computer Engineering Department, Tarbiat Modarres University, P.O. Box 14115-143, Tehran, Iran
Venue:
Signal Processing
Year:
2007

Citing 14
Cited 2

Adaptive signal processing

Adaptive signal processing
Adaptive filter theory

Adaptive filter theory
Adaptive signal processing algorithms: stability and performance

Adaptive signal processing algorithms: stability and performance
Acoustic signal processing for telecommunication

Acoustic signal processing for telecommunication
A time-domain feedback analysis of filtered-error adaptive gradientalgorithms

IEEE Transactions on Signal Processing
A feedback approach to the steady-state performance of fractionallyspaced blind adaptive equalizers

IEEE Transactions on Signal Processing
The behavior of LMS and NLMS algorithms in the presence ofspherically invariant processes

IEEE Transactions on Signal Processing
Convergence analysis of the binormalized data-reusing LMS algorithm

IEEE Transactions on Signal Processing
On the convergence behavior of the LMS and the normalized LMSalgorithms

IEEE Transactions on Signal Processing
A unified approach to the steady-state and tracking analyses ofadaptive filters

IEEE Transactions on Signal Processing
A family of adaptive filter algorithms with decorrelatingproperties

IEEE Transactions on Signal Processing
Transient analysis of data-normalized adaptive filters

IEEE Transactions on Signal Processing
Mean-square performance of a family of affine projection algorithms

IEEE Transactions on Signal Processing
Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data

IEEE Transactions on Information Theory

On the application of a unified adaptive filter theory in the performance prediction of adaptive filter algorithms

Digital Signal Processing
Steady-state mean-square performance analysis of a relaxed set-membership NLMS algorithm by the energy conservation argument

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

The independence assumptions are widely used conditions in the performance analysis of adaptive filters. Although not valid in general, because of the tapped-delay-line structure of the regression data in most filter implementations, its value lies in the simplifications, it introduces into the analysis. Another approach to study the performance of adaptive filters without using the independence assumptions, is to rely on averaging analysis. In this paper, we present a unified approach to study the steady-state performance of a family of affine projection and data-reusing adaptive algorithms based on the theory of averaging analysis and energy conservation relation without using the independence assumptions and assume specific models for the regression data. Finally, we provide several simulations results to evaluate the steady-state performance of a family of affine projection and data-reusing adaptive algorithms with and without using the independence assumptions.