Journal
APPLIED MATHEMATICS AND COMPUTATION
Volume 270, Issue -, Pages 511-526Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.amc.2015.08.054
Keywords
Adaptive filter; Affine projection algorithm; Statistical analysis; System identification
Categories
Funding
- National Natural Science Foundation of China [61201321, 61471300]
- Natural Science Basic Research Plan in Shaanxi Province of China [2014JQ8356]
- Fundamental Research Funds for the Central Universities [3102014JCQ01063]
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Class of algorithms referring to the affine projection algorithms (APA) applies updates to the weights in a direction that is orthogonal to the most recent input vectors. This speeds up the convergence of the algorithm over that of the normalized least mean square (NLMS) algorithm, especially for highly colored input processes. In this paper a new statistical analysis model is used to analyze the APA class of algorithms with unity step size. Four assumptions are made, which are based on the direction vector for the APA class. Under these assumptions, deterministic recursive equations for the weight error and for the mean-square error are derived. We also analyze the steady-state behavior of the APA class. The new model is applicable to input processes that are autoregressive as well as autoregressive-moving average, and therefore is useful under more general conditions than previous models for prediction of the mean square error of the APA class. Simulation results are provided to corroborate the analytical results. (C) 2015 Elsevier Inc. All rights reserved.
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