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ChenjianRAN,ZiliDENG
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Received:May 31, 2007Revised:April 10, 2008
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Decoupled Wiener state fuser for descriptor systems
Chenjian RAN, Zili DENG
(Department of Automation, Heilongjiang University, Harbin Heilongjiang 150080, China)
Abstract:
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