Title :
A New Optimal Weighted Measurement Fusion Kalman Filtering Algorithm
Author :
Xiaojun Sun ; Guangming Yan ; Bo Zhang
Author_Institution :
Dept. of Autom., Heilongjiang Univ., Harbin, China
Abstract :
Under the optimal fusion criterion of linear unbiased minimum variance, a new optimal weighted measurement fusion Kalman filtering algorithm is presented. It is applicable to the multisensor linear discrete time-invariant systems with correlated noises and different measurement matrices. Its optimality is rigorously proved. Compared with the existing results, the full-rank decomposition of matrix is avoided. A simulation example for the target tracking system shows the its effectiveness.
Keywords :
Kalman filters; matrix decomposition; sensor fusion; statistical analysis; Kalman filtering algorithm; correlated noise; full-rank matrix decomposition; linear unbiased minimum variance; measurement matrix; multisensor linear discrete time-invariant systems; optimal weighted measurement fusion criterion; Equations; Filtering algorithms; Kalman filters; Mathematical model; Noise; Noise measurement; Weight measurement; Kalman filtering; global optimality; multisensor information fusion; weighted measurement fusion;
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2014 Sixth International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-4956-4
DOI :
10.1109/IHMSC.2014.114
