@inproceedings{78c1d80ccf4447b9a0dae6174c804551,
title = "Diffusion Bias-Compensated Feature Sparse LMS Algorithms Over Networks",
abstract = "In recent years, distributed strategies are widely used in sparse parameter estimation of FIR filter. In some cases, the parameter may be non-zero only in one or several regions, meanwhile, if the input signal is polluted by noise, using traditional least-mean-square algorithm to estimate the parameter is biased. We propose the bias-compensated feature sparse least-mean-square algorithm in this paper, for exploiting the sparsity feature of the system and removing the bias by noisy. The superiority of our algorithm over the traditional methods is shown by the simulation results.",
keywords = "adaptive filtering, bias-compensated, diffusion strategy, least-mean-square, sparsity feature",
author = "Ziyi Wang and Lijuan Jia",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 China Automation Congress, CAC 2021 ; Conference date: 22-10-2021 Through 24-10-2021",
year = "2021",
doi = "10.1109/CAC53003.2021.9728071",
language = "English",
series = "Proceeding - 2021 China Automation Congress, CAC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6311--6314",
booktitle = "Proceeding - 2021 China Automation Congress, CAC 2021",
address = "United States",
}