Title :
Weighted measurement fusion Kalman filter based on linear unbiased minimum variance criterion
Author :
Gao, Yuan ; Ran, Chenjian ; Deng, Zili
Author_Institution :
Dept. of Autom., Heilongjiang Univ., Harbin, China
Abstract :
For the multisensor systems with correlated noises and identical measurement matrices, based on linear unbiased minimum variance (LUMV) criterion, a weighted measurement fusion (WMF) Kalman filter is presented, where the optimal weights is given by the Lagrange multiplier method. By using the information filter, it is proved that it is functionally equivalent to the centralized fusion Kalman filter, i.e. it is numerically identical to the centralized fusion Kalman filter, so that they have the global optimality. In order to reduce the computational burden, another simple algorithm for computing the optimal weights is also derived, and comparison of computational counts of two algorithms for computing optimal weights is given. A numerical simulation example verifies their functional equivalence. The proposed results can be applied to solve the information fusion filtering problem for the autoregressive moving average (ARMA) signals.
Keywords :
Kalman filters; autoregressive moving average processes; matrix algebra; sensor fusion; Lagrange multiplier method; autoregressive moving average signals; centralized fusion Kalman filter; correlated noises; identical measurement matrices; information filter; linear unbiased minimum variance criterion; multisensor systems; weighted measurement fusion Kalman filter; Automation; Information filters; Laboratories; Lagrangian functions; Multisensor systems; Noise measurement; Numerical simulation; Radio access networks; State estimation; Weight measurement;
Conference_Titel :
Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3871-6
Electronic_ISBN :
0191-2216
DOI :
10.1109/CDC.2009.5399691