Bias-compensated normalised LMS algorithm with noisy input
作者： B. Kang , J. Yoo , P. Park
DOI: 10.1049/EL.2013.0246
关键词: Constraint (information theory) 、 Least mean squares filter 、 Control theory 、 Function (mathematics) 、 Estimation theory 、 Weight coefficient 、 Adaptive filter 、 Mathematics 、 Noise 、 Least mean square algorithm
摘要: A new bias‐compensated normalised least mean square (NLMS) algorithm for parameter estimation with a noisy input is proposed. The algorithm is obtained from an approximated cost function based on the statistical properties of the input noise and involves a condition checking constraint to decide whether the weight coefficient vector must be updated. Simulation results show that the proposed algorithm is more robust and accurate than the conventional method.
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