DocumentCode
728491
Title
Design of a robust fusion of probability densities
Author
Ajgl, Jiri ; Simandl, Miroslav
Author_Institution
Dept. of Cybern., Univ. of West Bohemia, Pilsen, Czech Republic
fYear
2015
fDate
1-3 July 2015
Firstpage
4204
Lastpage
4209
Abstract
The paper deals with the fusion of probability densities. A selection of the weights of the weighted geometric mean of densities is justified, as well as the selection of the geometric mean itself, from a more general perspective. It is shown that the Chernoff fusion provides the density that minimises the greatest Kullback-Leibler divergence to the densities that are being fused. The interpretation of the densities is discussed and finally, illustrative examples are provided.
Keywords
probability; sensor fusion; Chernoff fusion; Kullback-Leibler divergence; probability densities; robust fusion; weighted geometric mean; Bayes methods; Cognition; Estimation; Optimization; Probability density function; Robustness; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference (ACC), 2015
Conference_Location
Chicago, IL
Print_ISBN
978-1-4799-8685-9
Type
conf
DOI
10.1109/ACC.2015.7171989
Filename
7171989
Link To Document