A new perspective on the convergence and stability of NLMS Hammerstein filters

This paper is devoted to the analysis of the convergence and stability of adaptive Hammerstein filters using the normalized least-mean-square (NLMS) algorithm. Such an analysis provides a new perspective on the update process of Hammerstein filters by focusing on the simultaneous update of the two c...

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Bibliographic Details
Published in2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA) pp. 343 - 348
Main Authors Ortiz Batista, Eduardo Luiz, Seara, Rui
Format Conference Proceeding
LanguageEnglish
Published University of Trieste and University of Zagreb 01.09.2013
Subjects
Adaptive filters
Convergence
Filtering algorithms
Filtering theory
Finite impulse response filters
Hammerstein filters
least-mean-square algorithms
Maximum likelihood detection
Nonlinear filters
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Summary:This paper is devoted to the analysis of the convergence and stability of adaptive Hammerstein filters using the normalized least-mean-square (NLMS) algorithm. Such an analysis provides a new perspective on the update process of Hammerstein filters by focusing on the simultaneous update of the two cascaded structures (nonlinearity and linear filter) composing these filters. In this context, it is shown that the impact of the simultaneous update, which is often overlooked in the open literature, is of fundamental importance for choosing the adaptive algorithm parameters and, thus, to ensure the algorithm stability and obtain faster convergence. Simulation results confirm the effectiveness of the design guidelines obtained using the proposed analysis approach.
ISSN:1845-5921
DOI:10.1109/ISPA.2013.6703764