• 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