Set-membership NLMS algorithm based on bias-compensated and regression noise variance estimation for noisy inputs
Systems and Control
2018-04-20 v2
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.
Cite
@article{arxiv.1804.06034,
title = {Set-membership NLMS algorithm based on bias-compensated and regression noise variance estimation for noisy inputs},
author = {Kaili Yin and Haiquan Zhao and Lu Lu},
journal= {arXiv preprint arXiv:1804.06034},
year = {2018}
}