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2013 (Vol. 6, Issue: 10)
Article Information:

Covariance Intersection Fusion Kalman Estimators for Multi-Sensor System with Colored Measurement Noises

Wen-Juan Qi, Peng Zhang and Zi-Li Deng
Corresponding Author:  Wen-Juan Qi 

Key words:  Covariance intersection fusion, colored measurement noises, the centralized fusion, weighted fusion , , ,
Vol. 6 , (10): 1872-1878
Submitted Accepted Published
November 20,2012 January 11, 2013 July 20, 2013
Abstract:

For multi-sensor system with colored measurement noises, using the observation transformation, the system can be converted into an equivalent system with correlated measurement noises. Based on this method, using the classical Kalman filtering, this study proposed a Covariance Intersection (CI) fusion Kalman estimator, which can handle the fused filtering, prediction and smoothing problems. The advantage of the proposed method is that it can avoid the computation of the cross-covariances among the local filtering errors and can reduce the computational burden significantly, as well as the CI fusion algorithm can be used in the uncertain system with unknown cross-covariances. Based on classical Kalman filtering theory, the centralized fusion and three weighted fusion (weighted by matrices, scalars and diagonal) estimators are also presented respectively. Their accuracy comparisons are given. The geometric interpretations based on covariance ellipses are also given. The experiment results show that the accuracy of the CI fuser is higher than that of the each local smoothers and is lower that that of the centralized fusion Kalman smoother or the optimal fuser weighted by matrix. The MSE curves show that the accuracy of the CI fuser is close to the optimal fuser weighted by matrix in most instances, which means that our proposed method has higher accuracy and good performance.
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  Cite this Reference:
Wen-Juan Qi, Peng Zhang and Zi-Li Deng, 2013. Covariance Intersection Fusion Kalman Estimators for Multi-Sensor System with Colored Measurement Noises.  Research Journal of Applied Sciences, Engineering and Technology, 6(10): 1872-1878.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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