Multisensor information fusion Wiener state estimators for descriptor systems

摘要

By the modern time-series analysis method,based on autoregressive moving average (ARMA)innovation model and white noise estimation theory,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.