Multi-sensor optimal fusion fixed-interval Kalman smoothers

Abstract

Based on the optimal weighted fusion algorithms in the linear minimum variance sense, the optimal fusion fixed-interval Kalman smoothers are given for discrete time-varying linear stochastic control systems with multiple sensors and correlated noises, which have a three-layer fusion structure. The first and the second fusion layers both have netted parallel structures to determine the cross-covariance matrices of prediction and smoothing errors between any two-sensor subsystems, respectively. The third fusion layer is the fusion centre to determine the optimal weights and obtain the optimal fusion fixed-interval smoothers. Smoothing error cross-covariance matrix between any two-sensor subsystems is derived. Applying it to a tracking system with three-sensors shows the effectiveness.