Stability of recursive stochastic tracking algorithms

Author

Guo, Lei

Author_Institution

Inst. of Syst. Sci., Acad. Sinica, Beijing, China

fYear

1993

fDate

15-17 Dec 1993

Firstpage

2062

Abstract

The paper presents a stability study for the random linear equation x_n+1=(I-A_n)x_n. It is shown that for a quite general class of random matrices {A_n} of interest, the stability of such a vector equation can be guaranteed by that of a corresponding scalar linear equation, for which various results are given without requiring stationary or mixing conditions. Then, these results are applied to the estimation of time varying parameters in linear stochastic systems, giving a unified stability condition for various tracking algorithms including the standard Kalman filter, least mean squares, and least squares with forgetting factor

Keywords

Kalman filters; least squares approximations; matrix algebra; parameter estimation; stability; stochastic systems; time-varying systems; tracking; Kalman filter; forgetting factor; least mean squares; linear stochastic systems; random linear equation; random matrices; recursive stochastic tracking; scalar linear equation; stability; time varying parameter estimation; vector equation; Equations; Least squares approximation; Resonance light scattering; Signal processing algorithms; Stability; Stochastic processes; Stochastic resonance; Stochastic systems; Time varying systems; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1993., Proceedings of the 32nd IEEE Conference on

Conference_Location

San Antonio, TX

Print_ISBN

0-7803-1298-8

Type

conf

DOI

10.1109/CDC.1993.325562

Filename

325562