A New Local Measure of Disagreement between Belief Functions – Application to Localization

  • Arnaud Roquel
  • Sylvie Le Hégarat-Mascle
  • Isabelle Bloch
  • Bastien Vincke
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)


In the theory of belief functions, the disagreement between sources is often measured in terms of conflict or dissimilarity. These measures are global to the sources, and provide few information about the origin of the disagreement. We propose in this paper a “finer” measure based on the decomposition of the global measure of conflict (or distance). It allows focusing the measure on some hypotheses of interest (namely the ones likely to be chosen after fusion).We apply the proposed so called “local” measures of conflict and distance to the choice of sources for vehicle localization.We show that considering sources agreement/disagreement outperforms blind fusion.


Movement Estimation Local Measure Rotational Component Belief Function Canonical Decomposition 
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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  1. 1.
    Bak, A., Bouchafa, S., Aubert, D.: Detection of independently moving objects through stereo vision and ego-motion extraction. In: Intelligent Vehicles Symposium (IV), pp. 863–870. IEEE (2010)Google Scholar
  2. 2.
    Denoeux, T.: Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence. Artificial Intelligence 172(2-3), 234–264 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Dubois, D., Prade, H.: Representation and combination of uncertainty with belief functions and possibility measures. Computational Intelligence 4(3), 244–264 (1988)CrossRefGoogle Scholar
  4. 4.
    Jousselme, A.L., Maupin, P.: Distances in evidence theory: Comprehensive survey and generalizations. International Journal of Approximate Reasoning 53(2), 118–145 (2012)CrossRefGoogle Scholar
  5. 5.
    Liu, W.: Analyzing the degree of conflict among belief functions. Artificial Intelligence 170(11), 909–924 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Martin, A., Jousselme, A.L., Osswald, C.: Conflict measure for the discounting operation on belief functions. In: The 11th Annual Conference on Information Fusion, pp. 1–8. IEEE, Cologne, Germany (2008)Google Scholar
  7. 7.
    Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: A factored solution to the simultaneous localization and mapping problem. In: National Conference on Artificial Intelligence, pp. 593–598. AAAI, Menlo Park (2002)Google Scholar
  8. 8.
    Schubert, J.: Conflict management in Dempster-Shafer theory using the degree of falsity. International Journal of Approximate Reasoning 52(3), 449–460 (2011)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Smets, P.: The canonical decomposition of a weighted belief. In: 14th International Joint Conference on Artificial intelligence, pp. 1896–1901. Morgan Kaufmann Publishers Inc., San Francisco (1995)Google Scholar
  10. 10.
    Smets, P.: Analyzing the combination of conflicting belief functions. Information Fusion 8(4), 387–412 (2007)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Arnaud Roquel
    • 1
  • Sylvie Le Hégarat-Mascle
    • 1
  • Isabelle Bloch
    • 2
  • Bastien Vincke
    • 1
  1. 1.Université Paris Sud, IEFOrsayFrance
  2. 2.Télécom ParisTech, CNRS LTCIParisFrance

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