TY - JFULL
AU - Mohammad Shams Esfand Abadi and John Hakon Husøy
PY - 2008/12/
TI - Mean-Square Performance of Adaptive Filter Algorithms in Nonstationary Environments
T2 - International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering
SP - 2585
EP - 2592
EM - John.H.Husoy@uis.no
VL - 2
SN - 1307-6892
UR - http://waset.org/publications/9212
PU - World Academy of Science, Engineering and Technology
NX - International Science Index 23, 2008
N2 - Employing a recently introduced unified adaptive filter
theory, we show how the performance of a large number of important
adaptive filter algorithms can be predicted within a general framework
in nonstationary environment. This approach is based on energy conservation
arguments and does not need to assume a Gaussian or white
distribution for the regressors. This general performance analysis can
be used to evaluate the mean square performance of the Least Mean
Square (LMS) algorithm, its normalized version (NLMS), the family
of Affine Projection Algorithms (APA), the Recursive Least Squares
(RLS), the Data-Reusing LMS (DR-LMS), its normalized version
(NDR-LMS), the Block Least Mean Squares (BLMS), the Block
Normalized LMS (BNLMS), the Transform Domain Adaptive Filters
(TDAF) and the Subband Adaptive Filters (SAF) in nonstationary
environment. Also, we establish the general expressions for the
steady-state excess mean square in this environment for all these
adaptive algorithms. Finally, we demonstrate through simulations that
these results are useful in predicting the adaptive filter performance.
ER -