TY - JFULL AU - Mohammad Shams Esfand Abadi and John Hakon Hus酶y PY - 2008/12/ TI - Mean-Square Performance of Adaptive Filter Algorithms in Nonstationary Environments T2 - International Journal of Electronics and Communication Engineering SP - 2585 EP - 2592 VL - 2 SN - 1307-6892 UR - https://publications.waset.org/pdf/9212 PU - World Academy of Science, Engineering and Technology NX - Open 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 -