Title :
Stability of recursive stochastic tracking algorithms
Author_Institution :
Inst. of Syst. Sci., Acad. Sinica, Beijing, China
Abstract :
The paper presents a stability study for the random linear equation xn+1=(I-An)xn. It is shown that for a quite general class of random matrices {An} 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;
Conference_Titel :
Decision and Control, 1993., Proceedings of the 32nd IEEE Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-1298-8
DOI :
10.1109/CDC.1993.325562
