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Hierarchical Proportional Redistribution for bba Approximation

  • Jean Dezert
  • Deqiang Han
  • Zhunga Liu
  • Jean-Marc Tacnet
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)

Abstract

Dempster’s rule of combination is commonly used in the field of information fusion when dealing with belief functions. However, it generally requires a high computational cost. To reduce it, a basic belief assignment (bba) approximation is needed. In this paper we present a new bba approximation approach called hierarchical proportional redistribution (HPR) allowing to approximate a bba at any given level of non-specificity. Two examples are given to show how our new HPR works.

Keywords

High Computational Cost Information Fusion Belief Function Evidence Theory Outer Approximation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jean Dezert
    • 1
  • Deqiang Han
    • 2
  • Zhunga Liu
    • 3
  • Jean-Marc Tacnet
    • 4
  1. 1.The French Aerospace LabPalaiseauFrance
  2. 2.Inst. of Integrated AutomationXi’an Jiaotong UniversityXi’anChina
  3. 3.School of AutomationNorth-Western Polytechnical UniversityXi’anChina
  4. 4.Irstea, UR ETGRSt-Martin-d’HèresFrance

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