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Proportionate NLMS With Unbiasedness Criterion for Sparse System Identification in the Presence of Input and Output Noises

机译:输入和输出噪声存在下具有无偏准则的比例NLMS用于稀疏系统识别

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摘要

This brief proposes a bias-compensated proportionate normalized least mean square (BCPNLMS) method for identifying sparse system when subjected to the noisy input. The proposed BCPNLMS algorithm, which combines the proportionate scheme and the unbiasedness criterion, is able to identify the system parameters with better steady-state accuracy and faster convergence speed than conventional NLMS, bias-compensated NLMS, and PNLMS algorithms. Robustness and high identification accuracy with noisy input can be achieved by introducing the bias-compensation term derived from the unbiasedness criterion. Simulation results on sparse system identification confirm the excellent performance of the proposed BCPNLMS in the presence of both input and output noises.
机译:本摘要提出了一种偏置补偿的比例归一化最小均方(BCPNLMS)方法,用于在受到噪声输入时识别稀疏系统。提出的BCPNLMS算法结合了比例方案和无偏准则,与常规NLMS,偏置补偿NLMS和PNLMS算法相比,能够以更高的稳态精度和更快的收敛速度识别系统参数。通过引入从无偏度准则中得出的偏置补偿项,可以实现带有噪声输入的鲁棒性和高识别精度。稀疏系统识别的仿真结果证实了在存在输入和输出噪声的情况下,所提出的BCPNLMS的出色性能。

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