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  • Research Article
  • Open Access

Mean-Square Performance Analysis of the Family of Selective Partial Update NLMS and Affine Projection Adaptive Filter Algorithms in Nonstationary Environment

EURASIP Journal on Advances in Signal Processing20102011:484383

https://doi.org/10.1155/2011/484383

  • Received: 30 June 2010
  • Accepted: 11 October 2010
  • Published:

Abstract

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.

Keywords

  • Information Technology
  • Energy Conservation
  • Quantum Information
  • General Framework
  • Tracking Performance

Publisher note

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Authors’ Affiliations

(1)
Faculty of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University, P.O. Box 16785-163, Tehran, Iran

Copyright

© M. Shams Esfand Abadi and F. Moradiani. 2011

This article is published under license to BioMed Central Ltd. 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.

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