Automatica (Journal of IFAC)
A unified approach to optimal state estimation for stochastic singular systems
Automatica (Journal of IFAC)
Wiener filter design using polynomial equations
IEEE Transactions on Signal Processing
H2 inferential filtering, prediction, and smoothing withapplication to rolling mill gauge estimation
IEEE Transactions on Signal Processing
Decoupled distributed Kalman fuser for descriptor systems
Signal Processing
Self-tuning decoupled information fusion Wiener state component filters and their convergence
Automatica (Journal of IFAC)
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
New approach to information fusion steady-state Kalman filtering
Automatica (Journal of IFAC)
Hi-index | 22.15 |
Based on the modern time-series analysis method, a new time-domain Wiener filtering approach is presented. Asymptotically stable Wiener state estimators are presented for discrete linear stochastic descriptor systems. They can be implemented via the autoregressive moving average (ARMA) recursive filters. They can handle the optimal state filtering, smoothing, and prediction problems in a unified framework, and can simply be obtained based on the ARMA innovation model. The solution of the Diophantine equations and Riccati equations is avoided, so that the computational burden is reduced. A simulation example shows the effectiveness of the new approach.