@inproceedings{13f58aa3bf9d41678b89ddf64e162339,
title = "Distributed bias-compensated recursive least squares estimation over multi-agent networks",
abstract = "We study the problem of distributed parameter estimation over multi-agent networks, where agents collaborate to estimate a common parameter vector. Considering the situation where both the input and the output of each agent are corrupted by additive noise, traditional recursive least-squares (RLS) algorithms are biased. The stand-alone bias-compensated RLS (BCRLS) algorithm can remove the effect of noise-induced bias but increases the variance of the local estimator. It has been discussed that the variance can be significantly reduced by distributed cooperation. Therefore, a series of distributed BCRLS algorithms are proposed based on current mainstream distributed cooperation schemes (incremental, consensus and diffusion schemes). And performance of the proposed distributed algorithms is compared. Simulation results show that distributed BCRLS estimation performs better than the noncooperative BCRLS (Nco-BCRLS) estimation. And the diffusion BCRLS (Diff-BCRLS) algorithm has the best performance in estimation accuracy and tracking capability among the distributed algorithms.",
keywords = "Bias Compensation, Distributed Parameter Estimation, Distributed Strategies, Least-Squares, Multi-Agent Networks",
author = "Jian Lou and Lijuan Jia and Yangzhi Ye and Zijiang Yang",
note = "Publisher Copyright: {\textcopyright} 2016 TCCT.; 35th Chinese Control Conference, CCC 2016 ; Conference date: 27-07-2016 Through 29-07-2016",
year = "2016",
month = aug,
day = "26",
doi = "10.1109/ChiCC.2016.7554626",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "7996--8001",
editor = "Jie Chen and Qianchuan Zhao and Jie Chen",
booktitle = "Proceedings of the 35th Chinese Control Conference, CCC 2016",
address = "United States",
}