Optimal multi-sensor Kalman smoothing fusion for discrete multichannel ARMA signals

Abstract

Based on the multi-sensor optimal information fusion criterion weighted by matrices in the linear minimum variance sense, using white noise estimators, an optimal fusion distributed Kalman smoother is given for discrete multi-channel ARMA (autoregressive moving average) signals. The smoothing error cross-covariance matrices between any two sensors are given for measurement noises. Furthermore, the fusion smoother gives higher precision than any local smoother does.