Title :
Optimal recursive filtering, prediction, and smoothing for singular stochastic discrete-time systems
Author :
Zhang, Huanshui ; Xie, Lihua ; Soh, Yeng Chai
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
fDate :
11/1/1999 12:00:00 AM
Abstract :
A new and simple approach to optimal recursive filtering, prediction, and smoothing for singular stochastic discrete-time systems is presented by using a time-domain innovation analysis method. The estimators are calculated based on an ARMA innovation model which can be obtained using spectral factorization or a recursive identifier. The prediction problem for the singular systems is solved with the aid of an output predictor. Further, a simple solution is presented for the complex smoothing problem
Keywords :
autoregressive moving average processes; discrete time systems; identification; optimisation; prediction theory; recursive filters; smoothing methods; spectral analysis; stochastic systems; time-domain analysis; ARMA innovation model; estimators; optimal recursive filtering; optimal recursive prediction; optimal recursive smoothing; singular stochastic discrete-time systems; singular systems; time-domain innovation analysis method; Asymptotic stability; Filtering; Filters; Noise measurement; Smoothing methods; State estimation; Stochastic resonance; Stochastic systems; Technological innovation; Time domain analysis;
Journal_Title :
Automatic Control, IEEE Transactions on
