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Received:May 31, 2007Revised:April 10, 2008
Decoupled Wiener state fuser for descriptor systems
Chenjian RAN, Zili DENG
(Department of Automation, Heilongjiang University, Harbin Heilongjiang 150080, China)
By the modem time series analysis method, based on the autoregressive moving average (ARMA) innovation models and white noise estimation theory, using the optimal fusion rule weighted by diagonal matrices, a distributed descriptor Wiener state fuser is presented by weighting the local Wiener state estimators for the linear discrete stochastic descriptor systems with multisensor. It realizes a decoupled fusion estimation for state components. In order to computethe optimal weights, the formulas of computing the cross-covariances among local estimation errors are presented based on cross-covariances among the local innovation processes, input white noise, and measurement white noises. 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 and correctness.
Key words:  Multisensor information fusion  Weighted fusion  Decoupled fusion  Descriptor system  Wiener state fuser  White noise estimator  ARMA innovation model  Modern time series analysis method