DocumentCode :
3007895
Title :
Improvement of Fusion Algorithm Based on Evidence Theory
Author :
Chen Yi ; Wang Gai-Yun ; Li Bing
Author_Institution :
Dept. of Comput. Sci., Inst. of Electron. Ind., Guilin
fYear :
2008
fDate :
25-26 Sept. 2008
Firstpage :
539
Lastpage :
542
Abstract :
The relationship between basic probability assignment (BPA) and belief function in evidence theory is studied. The idea that belief function is regarded as BPA in data fusion is firstly introduced in this paper. An improved fusion algorithm based on distributed fusion method is put forward using this idea. The improved algorithm is called ´distributed fusion algorithm based on belief function assignment´. Through the experimental simulations to the traditional distributed fusion algorithm and the improved algorithm, the results show that the two algorithms are both valid in property identification.
Keywords :
belief networks; pattern recognition; sensor fusion; basic probability assignment; belief function; belief function assignment; distributed fusion algorithm; evidence theory; property identification; Bismuth; Computer science; Electronics industry; Genetics; Instruments; Observability; Sensor phenomena and characterization; Evidence Theory; belief function; fusion; identification frend or foe; probability assignment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Genetic and Evolutionary Computing, 2008. WGEC '08. Second International Conference on
Conference_Location :
Hubei
Print_ISBN :
978-0-7695-3334-6
Type :
conf
DOI :
10.1109/WGEC.2008.101
Filename :
4637503
Link To Document :
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