On convergence of a BCLS algorithm for noisy autoregressive process estimation

Author

Jin, Chun-Zhi ; Jia, Li-Juan ; Yang, Zi-Jiang ; Wada, Kiyoshi

Author_Institution

Dept. of Electr. & Electron. Syst. Eng., Kyushu Univ., Fukuoka, Japan

Volume

4

fYear

2002

fDate

10-13 Dec. 2002

Firstpage

4252

Abstract

The identification of AR processes whose measurement are corrupted by additive noise is considered. A bias compensated least squares (BCLS) algorithm is derived on the framework of solving nonlinear bias compensation equation (BCE). The framework is convenience for investigating the convergence property of the algorithm. Convergence analysis of the proposed algorithm is performed from the numerical analysis viewpoint. The algorithm is to find a fixed point of the BCE. By examination of the BCE and their Jacobian, a theoretical result is obtained to make clear that the relationship of convergence and the parameters of the AR process as well as the ratio of noise to signal. Based on the results of convergence analysis, it can be expected that more effective estimation algorithms are developed.

Keywords

algorithm theory; autoregressive processes; convergence; estimation theory; least squares approximations; noise; autoregressive process; bias compensated least squares algorithm; convergence; estimation algorithms; fixed point; noise to signal ratio; nonlinear bias compensation equation; numerical analysis; Additive noise; Algorithm design and analysis; Autoregressive processes; Convergence of numerical methods; Jacobian matrices; Least squares methods; Noise measurement; Nonlinear equations; Numerical analysis; Performance analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2002, Proceedings of the 41st IEEE Conference on

ISSN

0191-2216

Print_ISBN

0-7803-7516-5

Type

conf

DOI

10.1109/CDC.2002.1185038

Filename

1185038