Convergence and tracking analysis of a variable normalised LMF (XE-NLMF) algorithm

Authors:
Azzedine Zerguine;Mun K. Chan;Tareq Y. Al-Naffouri;Muhammad Moinuddin;Colin F. N. Cowan
Affiliations:
Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia;Delphi Automotive Systems, 501 Ang Mo Kio Ind, Park 1, 569621, Singapore;Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia;Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia;School of Electrical and Electronic Engineering, Queen's University of Belfast, Ashby Building, Stranmillis Road, BT9 5AH Belfast, UK
Venue:
Signal Processing
Year:
2009

Citing 9
Cited 2

Adaptive filter theory (3rd ed.)

Adaptive filter theory (3rd ed.)
Wireless Communications: Principles and Practice

Wireless Communications: Principles and Practice
Convergence and steady-state analysis of the normalized least mean fourth algorithm

Digital Signal Processing
Stochastic gradient adaptation under general error criteria

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

IEEE Transactions on Signal Processing
A variable step size LMS algorithm

IEEE Transactions on Signal Processing
Transient analysis of adaptive filters with error nonlinearities

IEEE Transactions on Signal Processing
An improved statistical analysis of the least mean fourth (LMF) adaptive algorithm

IEEE Transactions on Signal Processing
The least mean fourth (LMF) adaptive algorithm and its family

IEEE Transactions on Information Theory

A noise constrained least mean fourth (NCLMF) adaptive algorithm

Signal Processing
Modified NLMF adaptation of Volterra filters used for nonlinear acoustic echo cancellation

Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

The least-mean-fourth (LMF) algorithm is known for its fast convergence and lower steady state error, especially in sub-Gaussian noise environments. Recent work on normalised versions of the LMF algorithm has further enhanced its stability and performance in both Gaussian and sub-Gaussian noise environments. For example, the recently developed normalised LMF (XE-NLMF) algorithm is normalised by the mixed signal and error powers, and weighted by a fixed mixed-power parameter. Unfortunately, this algorithm depends on the selection of this mixing parameter. In this work, a time-varying mixed-power parameter technique is introduced to overcome this dependency. A convergence analysis, transient analysis, and steady-state behaviour of the proposed algorithm are derived and verified through simulations. An enhancement in performance is obtained through the use of this technique in two different scenarios. Moreover, the tracking analysis of the proposed algorithm is carried out in the presence of two sources of nonstationarities: (1) carrier frequency offset between transmitter and receiver and (2) random variations in the environment. Close agreement between analysis and simulation results is obtained. The results show that, unlike in the stationary case, the steady-state excess mean-square error is not a monotonically increasing function of the step size.