Mohammad Shams Esfand Abadi and John Hakon Husøy MeanSquare Performance of Adaptive Filter Algorithms in Nonstationary Environments
2586 - 2592
2008
2
11
International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering http://waset.org/publications/9212
http://waset.org/publications/23
World Academy of Science, Engineering and Technology
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 DataReusing LMS (DRLMS), its normalized version
(NDRLMS), 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
steadystate 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.
International Science Index 23, 2008