Information fusion wiener filter for the multisensor multi-channel ARMA signals with time-delayed measurements

Abstract

For the multisensor multi-channel autoregressive moving average (ARMA) signals with time-delayed measurements, using the modern time series analysis method, based on the ARMA innovation model, under the linear minimum variance optimal weighted fusion rules, three distributed optimal information fusion Wiener filters weighted by matrices, diagonal matrices and scalars are presented, which can handle the fused filtering, prediction and smoothing problems in a unified form. They overcome the drawback that the augmented state method requires a large computational burden. They are locally optimal and are globally suboptimal. The accuracy of the fusers is higher than that of each local estimator. In order to compute the optimal weights, the formula of computing the cross-covariances among local estimation errors is given. A Monte Carlo simulation example for the target tracking system with time-delayed measurements and 3 sensors shows their effectiveness.