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arXiv · 1804.06034

Set-membership NLMS algorithm based on bias-compensated and regression noise variance estimation for noisy inputs

Kaili Yin·Haiquan Zhao·Lu Lu
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Abstract

The bias-compensated set-membership normalised LMS (BCSMNLMS) algorithm is proposed based on the concept of set-membership filtering, which incorporates the bias-compensation technique to mitigate the negative effect of noisy inputs. Moreover, an efficient regression noise variance estimation method is developed by taking the iterative-shrinkage method. Simulations in the context of system identification demonstrate that the misalignment of the proposed BCSM-NLMS algorithm is low for noisy inputs.

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Kaili Yin, Haiquan Zhao, Lu Lu. 2018-04-17. Set-membership NLMS algorithm based on bias-compensated and regression noise variance estimation for noisy inputs. https://arxiv.org/abs/1804.06034

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