tailieunhanh - Báo cáo hóa học: " Research Article Mean-Square Performance Analysis of the Family of Selective Partial Update NLMS and Affine Projection Adaptive Filter Algorithms in Nonstationary Environment"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Mean-Square Performance Analysis of the Family of Selective Partial Update NLMS and Affine Projection Adaptive Filter Algorithms in Nonstationary Environment | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 484383 11 pages doi 2011 484383 Research Article Mean-Square Performance Analysis of the Family of Selective Partial Update NLMS and Affine Projection Adaptive Filter Algorithms in Nonstationary Environment Mohammad Shams Esfand Abadi and Fatemeh Moradiani Faculty of Electrical and Computer Engineering Shahid Rajaee Teacher Training University P. O. Box 16785-163 Tehran Iran Correspondence should be addressed to Mohammad Shams Esfand Abadi mshams@ Received 30 June 2010 Revised 29 August 2010 Accepted 11 October 2010 Academic Editor Antonio Napolitano Copyright 2011 M. Shams Esfand Abadi and F. Moradiani. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. We present the general framework for mean-square performance analysis of the selective partial update affine projection algorithm SPU-APA and the family of SPU normalized least mean-squares SPU-NLMS adaptive filter algorithms in nonstationary environment. Based on this the tracking performance of Max-NLMS N-Max NLMS and the various types of SPU-NLMS and SPU-APA can be analyzed in a unified way. The analysis is based on energy conservation arguments and does not need to assume a Gaussian or white distribution for the regressors. We demonstrate through simulations that the derived expressions are useful in predicting the performances of this family of adaptive filters in nonstationary environment. 1. Introduction Mean-square performance analysis of adaptive filtering algorithms in nonstationary environments has been and still is an area of active research 1-3 . When the input signal properties vary with time the adaptive filters are able to track these variations. The aim of tracking performance analysis is to characterize this tracking .