A Gradient-Based Variable Step-Size Scheme for Kurtosis of Estimated Error
IEEE Signal Process. Lett.(2010)
摘要
This paper proposes a new variable step-size LMS (VSLMS) algorithm with an approach in which a gradient-based weighted average of a kurtosis of an estimated error signal is used to improve the drawback of a previous algorithm for application to an unknown channel estimation. The proposed scheme leads not only to the enhancement of the convergence rate, but also to robustness in terms of low-SNR environments. It could also lead to obtaining a lower misadjustment error.
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关键词
least mean square,least-mean-square (lms),variable step-size,kurtosis,adaptive signal processing,adaptive filters,least mean squares methods,gradient-based variable step-size scheme,estimated error,channel estimation,least squares approximation,convergence rate,signal processing,convergence,cost function,robustness,error correction,adaptive filter,steady state
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