OPTIMAL FUSION DISTRIBUTED FILTER FOR DISCRETE MULTICHANNEL ARMA SIGNALS

Abstract

Based on the multi-sensor optimal information fusion criterion weighted by matrices in the linear minimum variance sense, an optimal information fusion distributed Kalman filter is given for discrete multi-channel ARMA (autoregressive moving average) signals with correlated noises. When all subsystems have the steady-state filters, a steady-state information fusion filter is also given. It has the reduced computation burden compared with the optimal fusion filter. The precision of the fusion filter is higher than that of any local filter, but is lower than that of the centralized filter. The filtering error cross-covariance matrix between any two subsystems is given for discrete multichannel ARMA signals. Applying it to a four-channel ARMA signal system with three sensors shows its effectiveness.