Optimal information fusion distributed smoother for discrete multichannel ARMA signals

Abstract

Based on the multisensor optimal information fusion criterion weighted by matrices in the linear minimum variance sense, using estimators of white measurement noise, an optimal information fusion distributed Kalman smoother is given for discrete time multichannel autoregressive moving average (ARMA) signals with correlated noise. It has a three-layer fusion structure with a fault tolerant property. The first and the second fusion layers both have netted parallel structrues to determine cross-covariance matrices between any two faultless sensors. The third fusion layer is the fusion centre to determine the optimal weights and obtain the optimal fusion smoother. The fusion smoother has higher precision than that of any local smoother. Its effectiveness is shown by applying it to a double-channel signal system with three sensors.