Title
Weighted measurement fusion Kalman filter based on linear unbiased minimum variance criterion
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.
Year
DOI
Venue
2009
10.1109/CDC.2009.5399691
CDC
Keywords
Field
DocType
identical measurement matrices,kalman filters,information filter,matrix algebra,lagrange multiplier method,autoregressive moving average processes,centralized fusion kalman filter,correlated noises,autoregressive moving average signals,multisensor systems,linear unbiased minimum variance criterion,weighted measurement fusion kalman filter,sensor fusion,global optimization,mathematical model,minimum variance,moving average,noise,noise measurement,kalman filter,data mining,numerical simulation
Mathematical optimization,Alpha beta filter,Extended Kalman filter,Fast Kalman filter,Control theory,Filtering problem,Sensor fusion,Kalman filter,Ensemble Kalman filter,Invariant extended Kalman filter,Mathematics
Conference
ISSN
ISBN
Citations 
0191-2216 E-ISBN : 978-1-4244-3872-3
978-1-4244-3872-3
2
PageRank 
References 
Authors
0.38
2
3
Name
Order
Citations
PageRank
Yuan Gao1887.67
Chenjian Ran2244.13
Zi-li Deng351444.75