Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Adaptive Filters
Comparison of convex combination and affine combination of adaptive filters
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Steady-state MSE performance analysis of mixture approaches to adaptive filtering
IEEE Transactions on Signal Processing
Transient and steady-state analysis of the affine combination of two adaptive filters
IEEE Transactions on Signal Processing
Unbiased model combinations for adaptive filtering
IEEE Transactions on Signal Processing
Fast coupled adaptation for sparse impulse responses using a partial haar transform
IEEE Transactions on Signal Processing
A variable step size LMS algorithm
IEEE Transactions on Signal Processing
Mean-square performance of a convex combination of two adaptive filters
IEEE Transactions on Signal Processing
A robust variable step-size LMS-type algorithm: analysis andsimulations
IEEE Transactions on Signal Processing
An Affine Combination of Two LMS Adaptive Filters—Transient Mean-Square Analysis
IEEE Transactions on Signal Processing
Adaptive mixture methods based on Bregman divergences
Digital Signal Processing
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The affine combination of two adaptive filters that simultaneously adapt on the same inputs has been actively investigated. In these structures, the filter outputs are linearly combined to yield a performance that is better than that of either filter. Various decision rules can be used to determine the time-varying parameter for combining the filter outputs. A recently proposed scheme based on the ratio of error powers of the two filters has been shown by simulation to achieve nearly optimum performance. The purpose of this paper is to present a first analysis of the statistical behavior of this error power scheme for white Gaussian inputs. Expressions are derived for the mean behavior of the combination parameter and for the adaptive weight mean-square deviation. Monte Carlo simulations show good to excellent agreement with the theoretical predictions.