DocumentCode
406215
Title
Decomposed state-fusion estimation for multisensor data fusion system
Author
Jin Xue-ho ; Yues-song, LIN
Author_Institution
Coll. of Informatics & Electron., Zhejiang Inst. of Sci. & Technol., Hangzhou, China
Volume
1
fYear
2003
fDate
14-17 Dec. 2003
Firstpage
624
Abstract
Based on matrix theory, a new decomposed state fusion estimation algorithm is presented. The algorithm is optimal for a special data fusion system, in which the covariance matrix of correlated measurement noise is a Pei-Radman matrix and observation matrices are identical. The steady error of decomposed estimation covariance in other general system is decided by measurement matrix and measurement noise covariance matrix.
Keywords
Kalman filters; covariance matrices; noise; sensor fusion; correlated measurement noise; covariance matrix; decomposed state fusion estimation algorithm; matrix theory; multisensor data fusion system; Control systems; Covariance matrix; Intelligent control; Intelligent sensors; Matrix decomposition; Noise measurement; Sensor fusion; Sensor phenomena and characterization; Sensor systems; State estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on
Conference_Location
Nanjing
Print_ISBN
0-7803-7702-8
Type
conf
DOI
10.1109/ICNNSP.2003.1279352
Filename
1279352
Link To Document