@inproceedings{3e8f0269e42b4303bbe671daea123e27,
title = "Adaptive filtering of EIV-FIR system by solving eigenvalue problems",
abstract = "We study the problem of parameter estimation in the errors-in-variables-FIR (EIV-FIR) system where the input and output signals are both corrupted by additive noises. Since the least-mean-square (LMS) and the recursive-least-squares (RLS) algorithms are biased for EIV- FIR system, a new bias-compensated RLS (BCRLS) algorithm is proposed to get the unbiased estimate via solving eigenvalue problems of a matrix. The matrix is derived from the cross-correlation function between the LS error and the prediction error. Two auxiliary estimators are introduced to construct the prediction error. When the product of the auxiliary estimator and the system parameter is a minimal value, the proposed algorithm can deal well with this condition. Simulation results show the high accuracy, good tracking performance and robustness of the proposed algorithm in non-stationary EIV- FIR system.",
keywords = "Adaptive Filtering, Bias Compensation, EIV-FIR, Eigenvector, System Identification",
author = "Qi Tang and Lijuan Jia and Shunshoku Kanae and Zijiang Yang",
note = "Publisher Copyright: {\textcopyright} 2018 Technical Committee on Control Theory, Chinese Association of Automation.; 37th Chinese Control Conference, CCC 2018 ; Conference date: 25-07-2018 Through 27-07-2018",
year = "2018",
month = oct,
day = "5",
doi = "10.23919/ChiCC.2018.8482558",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "1782--1786",
editor = "Xin Chen and Qianchuan Zhao",
booktitle = "Proceedings of the 37th Chinese Control Conference, CCC 2018",
address = "United States",
}