DocumentCode :
2057061
Title :
Improving the convergence of adaptive Hammerstein filters
Author :
Batista, Eduardo Luiz Ortiz ; Seara, Rui
Author_Institution :
Dept. of Inf. & Stat., Fed. Univ. of Santa Catarina, Florianopolis, Brazil
fYear :
2013
fDate :
9-13 Sept. 2013
Firstpage :
1
Lastpage :
5
Abstract :
The implementation of adaptive Hammerstein filters involves updating the coefficients of two cascaded blocks, namely, a memoryless nonlinearity and a linear filter. Such an update process presents important numerical problems mainly due to the non-uniqueness of the coefficient values that lead to optimum performance. These problems can be circumvented by keeping constant (not adapting) one of the filter coefficients, which however may significantly slow down the convergence of the adaptive algorithm. In this context, this paper presents a novel approach to implement adaptive Hammerstein filters in which a coefficient normalization strategy is used to overcome the aforementioned numerical problems. Thus, enhanced convergence speed is obtained with a small increase in the computational burden. Simulation results are presented to corroborate the effectiveness of the proposed strategy.
Keywords :
adaptive filters; convergence of numerical methods; nonlinear filters; adaptive Hammerstein filters; cascaded blocks; coefficient normalization strategy; enhanced convergence speed; linear filter; memoryless nonlinearity; Adaptive filters; Filtering theory; Finite impulse response filters; Maximum likelihood detection; Nonlinear filters; Power filters; Vectors; Adaptive filters; Hammerstein filters; NLMS algorithm; nonlinear filters;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European
Conference_Location :
Marrakech
Type :
conf
Filename :
6811576
Link To Document :
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