A unified framework for adaptive filter algorithms with variable step-size
Computers and Electrical Engineering
An adaptive projected subgradient approach to learning in diffusion networks
IEEE Transactions on Signal Processing
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Distributed estimation over an adaptive incremental network based on the affine projection algorithm
IEEE Transactions on Signal Processing
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Normalized least mean squares algorithms for FIR adaptive filtering with or without the reuse of past information are known to converge often faster than the conventional least mean squares (LMS) algorithm. This correspondence analyzes an LMS-like algorithm: the binormalized data-reusing least mean squares (BNDR-LMS) algorithm. This algorithm, which corresponds to the affine projection algorithm for the case of two projections, compares favorably with other normalized LMS-like algorithms when the input signal is correlated. Convergence analyses in the mean and in the mean-squared are presented, and a closed-form formula for the mean squared error is provided for white input signals as well as its extension to the case of a colored input signal. A simple model for the input-signal vector that imparts simplicity and tractability to the analysis of second-order statistics is fully described. The methodology is readily applicable to other adaptation algorithms of difficult analysis. Simulation results validate the analysis and ensuing assumptions