a-posteriori estimation subject to a constraint on the squared norm of the weight vector difference, and then introducing an unbiasedness criterion to insert a bias compensation term in the update. Under common statistical assumptions, the mean and mean square behaviors of weight deviation are derived for the R-BC-LMS algorithm. In addition, we develop the estimator for the input and output noise variances. Simulations in channel estimation, vehicle handsfree echo cancellation, and direction-of-arrival estimation demonstrate that our method outperforms the competing algorithms.">

Robust Bias-Compensated LMS Algorithm: Design, Performance Analysis and Applications (original) (raw)

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