Title :
Descriptor Wiener state fuser
Author :
Ran, Chenjian ; Deng, Zili
Author_Institution :
Dept. of Autom., Heilongjiang Univ., Harbin, China
Abstract :
By the modem time series analysis method, based on the autoregressive moving average (ARMA) innovation model and white noise estimation theory, a new local descriptor Wiener state estimators are presented, and using the optimal fusion rule weighted by diagonal matrices, a distributed decoupled descriptor Wiener state fuser is presented for the linear discrete stochastic descriptor systems with multisensor. It realizes a decoupled fusion estimation for state components. The descriptor Wiener state fuser is obtained by weighting the local Wiener state estimators. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors are presented. It can handle the fused filtering, smoothing, and prediction problems in a unified framework. Its accuracy is higher than that of each local estimator. A Monte Carlo simulation example shows its effectiveness.
Keywords :
autoregressive moving average processes; discrete systems; filtering theory; linear systems; state estimation; stochastic systems; time series; Monte Carlo simulation; autoregressive moving average innovation model; cross-covariances; descriptor Wiener state fuser; diagonal matrices; linear discrete stochastic descriptor systems; modem time series analysis method; optimal fusion rule; white noise estimation theory; Autoregressive processes; Estimation error; Estimation theory; Modems; State estimation; Stochastic resonance; Stochastic systems; Technological innovation; Time series analysis; White noise; ARMA innovation model; Decoupled fusion; Descriptor system; Modern time series analysis method; Multisensor information fusion; Weighted fusion; White noise estimator; Wiener state fuser;
Conference_Titel :
Control and Decision Conference, 2009. CCDC '09. Chinese
Conference_Location :
Guilin
Print_ISBN :
978-1-4244-2722-2
Electronic_ISBN :
978-1-4244-2723-9
DOI :
10.1109/CCDC.2009.5194953