Conference Proceeding

Optimal Fusion Reduced-Order Kalman Filters Weighted by Scalars for Stochastic Singular Systems

Dept. of Autom., Heilongjiang Univ., Harbin
01/2007; DOI:10.1109/ICARCV.2006.345171 pp.1 - 6 In proceeding of: Control, Automation, Robotics and Vision, 2006. ICARCV '06. 9th International Conference on
Source: IEEE Xplore

ABSTRACT Based on the optimal fusion algorithm weighted by scalars in the linear minimum variance sense, a distributed optimal fusion reduced-order Kalman filter with scalar weights is presented for discrete-time stochastic singular systems with multiple sensors and correlated noises. It has higher accuracy than any local filter does. Compared with the distributed fusion filter weighted by matrices, it has lower accuracy but has reduced computational burden. Computation formula of cross-covariance matrix of the filtering errors between any two sensors is given. An example with three sensors shows the effectiveness

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Keywords

Computation formula
 
computational burden
 
discrete-time stochastic singular systems
 
distributed optimal fusion reduced-order Kalman filter
 
linear minimum variance sense
 
local filter
 
optimal fusion algorithm weighted
 

Shuli Sun