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

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.

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Author information

Correspondence to Mohammad Shams Esfand Abadi.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Keywords

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