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State fusion estimation for multilevel multisensor system

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2 Author(s)
Xuebo, Jin ; National Laboratory of Industrial Control Technology, Zhejiang University. Hangzhou 310027. P.R. China; College of Informatics and Electronics. Zhejiang Institute of Science and Technology. Hangzhou 310033, P.R. China ; Youxian, Sun

Based on the single sensor Kalman filtering equations, this paper presents two-level and three-level optimal centralized and distributed estimation algorithms for hierarchical multisensor systems. The solution shows that when the correlated matrix, the mean of noise, the control input, and the measurement error are all zero, the result in this paper turns out to be the standard algorithm discussed. Simulation shows that the mean of noise, the control input, and the measurement error will not change the estimation covariance and the estimation covariance fluctuates greatly when the cross-correlated matrix is similar to the covariance of process noise.

Published in:

Systems Engineering and Electronics, Journal of  (Volume:14 ,  Issue: 4 )