Multi-sensor optimal fusion fixed-interval Kalman smoothers

Authors:
Shu-Li Sun
Affiliations:
Department of Automation, Electronic Engineering College, Heilongjiang University, Road Xuefu, No. 74, Harbin, Heilongjiang 150080, China
Venue:
Information Fusion
Year:
2008

Citing 4
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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.