DocumentCode
3096442
Title
Fusion Predictors for Multisensor Discrete-Time Linear Systems
Author
Song, Ha Ryong ; Kim, Du Yong ; Shin, Vladimir
Author_Institution
Gwangju Inst. of Sci. & Technol., Gwangju
fYear
2007
fDate
5-8 Nov. 2007
Firstpage
2542
Lastpage
2547
Abstract
Two novel fusion predictors for linear dynamic systems with different types of observations are proposed. They are formed by summing of the local Kalman filters/predictors with matrix weights depending only on time instants. The relationships between them and the optimal Kalman predictor are discussed. High accuracy and computational efficiency of the fusion predictors are demonstrated on the first-order Markov process and the GMTI with multisensor environment.
Keywords
Kalman filters; Markov processes; discrete time systems; linear systems; matrix algebra; sensor fusion; first-order Markov process; fusion predictors; linear dynamic systems; local Kalman filters; matrix weights; multisensor discrete-time linear systems; optimal Kalman predictor; Aircraft navigation; Equations; Gaussian noise; Kalman filters; Linear systems; Prediction algorithms; Sensor fusion; Sensor systems; Sensor systems and applications; Signal processing algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Electronics Society, 2007. IECON 2007. 33rd Annual Conference of the IEEE
Conference_Location
Taipei
ISSN
1553-572X
Print_ISBN
1-4244-0783-4
Type
conf
DOI
10.1109/IECON.2007.4460053
Filename
4460053
Link To Document