Multisensor information fusion Wiener filter for ARMA signals

Abstract

By the modern time series analysis method and white noise estimators, based on the autoregressive moving average (ARMA) innovation model and augmented state space model, under the linear minimum variance optimal fusion rule weighted by scalars, a multisensor optimal distributed fusion Wiener filter is proposed for single channel ARMA signals with white and colored measurement noises. The formulas of computing local filtering error variances and cross-covariances are given, which are applied to compute optimal weighting coefficients. Compared with the single sensor case, the accuracy of the fused filter is improved. A simulated example shows its effectiveness.