An LMS adaptive second-order Volterra filter with a zeroth-order term: steady-state performance analysis in a time-varying environment

This article studies the steady-state performance of the least mean square (LMS) adaptive second-order Volterra filter (SOVF) with a zeroth-order term for Gaussian inputs. The mean-square-error (MSE) criterion is evaluated first. Then, SOV LMS algorithm-based updating equations are derived. Next, th...

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Bibliographic Details
Published in:IEEE transactions on signal processing 1999-03, Vol.47 (3), p.872-876
Main Authors: Sayadi, M., Fnaiech, F., Najim, M.
Format: Article
Language:English
Subjects:
Adaptive filters
Algorithm design and analysis
Algorithms
Applied sciences
Computer simulation
Convergence
Criteria
Degradation
Detection, estimation, filtering, equalization, prediction
Equations
Exact sciences and technology
Gaussian
Information, signal and communications theory
Least mean squares
Least squares approximation
Mathematical analysis
Mathematical models
Nonlinear filters
Performance analysis
Predictive models
Recursion
Signal and communications theory
Signal, noise
Steady-state
Telecommunications and information theory
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Summary:This article studies the steady-state performance of the least mean square (LMS) adaptive second-order Volterra filter (SOVF) with a zeroth-order term for Gaussian inputs. The mean-square-error (MSE) criterion is evaluated first. Then, SOV LMS algorithm-based updating equations are derived. Next, the steady-state performance of the recursions is analyzed for a random walk model for the unknown system parameters, and the steady-state excess MSE is evaluated. Finally, the theoretical performance predictions are shown to be in good agreement with simulation results, especially for small step sizes.
ISSN:1053-587X
1941-0476
DOI:10.1109/78.747794