Based On Modern Time Series Of Multi-sensor Information Fusion Kalman Filter With The Wiener Filter

By the modem time series analysis method, based on the ARMA innovation model, under the linear minimum variance optimal information fusion criterion, three distributed fusion steady-state optimal Kalman filters, predictors and smoothers weighted by matrices, scalars, and diagonal matrices are presented for multisensor systems with correlated input and observation noises, and with correlated observation noises. The Lyapunov equations and formulas of computing local filtering, predicting and smoothing error variances and covariances are given, which are applied to compute optimal weights. The corresponding three distributed fusion Wiener state estimators are also presented. And based on the transformation of the ARMA model to the state space model, by the augmented state method, the multisensor distributed optimal information fusion Wiener filter, predictor and smoother are proposed for the ARMA signals with white and colored measurement noises. And the multisensor optimal distributed information fusion Wiener deconvolution filter, predictor and smoother, are also proposed. The proposed methods avoid the Riccati equation and Diophantine equation and can reduce the on-line computational burden. Compared to the single sensor case, the estimation accuracy is improved. Many simulation examples show their effectiveness.