DocumentCode :
875994
Title :
A fast recursive least squares adaptive second order Volterra filter and its performance analysis
Author :
Lee, Junghsi ; Mathews, V. John
Author_Institution :
Dept. of Electr. Eng., Utah Univ., Salt Lake City, UT, USA
Volume :
41
Issue :
3
fYear :
1993
fDate :
3/1/1993 12:00:00 AM
Firstpage :
1087
Lastpage :
1102
Abstract :
A fast, recursive least squares (RLS) adaptive nonlinear filter modeled using a second-order Volterra series expansion is presented. The structure uses the ideas of fast RLS multichannel filters, and has a computational complexity of O(N3) multiplications, where N-1 represents the memory span in number of samples of the nonlinear system model. A theoretical performance analysis of its steady-state behaviour in both stationary and nonstationary environments is presented. The analysis shows that, when the input is zero mean and Gaussian distributed, and the adaptive filter is operating in a stationary environment, the steady-state excess mean-squared error due to the coefficient noise vector is independent of the statistics of the input signal. The results of several simulation experiments show that the filter performs well in a variety of situations. The steady-state behaviour predicted by the analysis is in very good agreement with the experimental results
Keywords :
adaptive filters; computational complexity; digital filters; filtering and prediction theory; least squares approximations; RLS adaptive nonlinear filter; coefficient noise vector; computational complexity; fast recursive least squares adaptive second order Volterra filter; multichannel filters; nonstationary environments; performance analysis; simulation; stationary environment; steady-state behaviour; steady-state excess mean-squared error; Adaptive filters; Computational complexity; Gaussian noise; Least squares methods; Nonlinear filters; Nonlinear systems; Performance analysis; Resonance light scattering; Signal analysis; Steady-state;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/78.205715
Filename :
205715
Link To Document :
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