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
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;
Conference_Titel :
Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
0-7803-7702-8
DOI :
10.1109/ICNNSP.2003.1279352
