Multisensor Optimal Information Fusion Input White Noise Deconvolution Estimators

2004 ◽  
Vol 34 (4) ◽  
pp. 1886-1893 ◽  
Author(s):  
S. Sun
Keyword(s):  
White Noise ◽  
Information Fusion ◽  
Optimal Information ◽  
Deconvolution Estimators

An Information Fusion Algorithm Based on Kalman Filtering

2013 ◽  
Vol 444-445 ◽  
pp. 1072-1076
Author(s):  
Xiu Hu Tan
Keyword(s):  
Mathematical Model ◽  
Information Fusion ◽  
Minimum Mean Square Error ◽  
Minimum Variance ◽  
Probability Method ◽  
Fusion Algorithm ◽  
Multisensor Systems ◽  
Optimal Information ◽  
Noise Statistics ◽  
The Mathematical Model

For the multisensor systems with unknown noise variances, by the statistics method, the mathematical model and the noise statistics are essential, and this limitation was settled by adaptive algorithm. The adaptive Kalman filter was proposed to solve the filtering problem of the system with unknown mathematical model or noise statistics in information fusion. Based on the probability method and the scalar weighting optimal information fusion criterion in the minimum variance sense, the algorithm can not only optimize the multi-channel data, but also obtain the minimum mean square error (MMSE) by introducing fusion equation, namely the algorithm is optimal under the sense of MMSE, and the error is the least than the original Kalman information fusion algorithm. The test result shows that the algorithm can precede information fusion effectively under the distributed acquisition system.

Multi-model information fusion Kalman filtering and white noise deconvolution

2010 ◽  
Vol 11 (2) ◽  
pp. 163-173 ◽  
Author(s):  
Xiao-Jun Sun ◽  
Yuan Gao ◽  
Zi-Li Deng ◽  
Chuang Li ◽  
Jia-Wei Wang
Keyword(s):  
White Noise ◽  
Kalman Filtering ◽  
Information Fusion
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