Title :
Optimal and self-tuning State estimation for singular stochastic systems: a polynomial equation approach
Author :
Zhang, Huanshui ; Xie, Lihua ; Soh, Yeng Chai ; Wang, Wei
Author_Institution :
HIT Campus, Shenzen Univ., China
fDate :
6/1/2004 12:00:00 AM
Abstract :
This paper is concerned with the optimal steady-state estimation for singular stochastic discrete-time systems using a polynomial equation approach. The key to the optimal estimation is the calculation of an optimal estimator gain matrix. The main contribution of the paper is the derivation of a simple method for computing the gain matrix. Our method involves solving one simple polynomial equation which is derived from the uniqueness of the autoregressive moving average (ARMA) innovation model. The approach covers prediction, filtering, and smoothing problems. Further, when the noise statistics of the model are not available, self-tuning estimation is performed by identifying one ARMA innovation model.
Keywords :
autoregressive moving average processes; discrete time systems; matrix algebra; polynomials; state estimation; stochastic systems; ARMA innovation model; autoregressive moving average model; discrete-time system; filtering problem; gain matrix computation; innovation analysis; noise statistics; optimal state estimation; polynomial equation approach; prediction problem; self-tuning estimation; singular stochastic system; smoothing problem; Autoregressive processes; Equations; Filtering; Polynomials; Smoothing methods; State estimation; Statistics; Steady-state; Stochastic systems; Technological innovation;
Journal_Title :
Circuits and Systems II: Express Briefs, IEEE Transactions on
DOI :
10.1109/TCSII.2004.829573