Multisensor data fusion in situation assessment processes

  • Alain Appriou
Invited Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1244)


To identify or localize a target, multisensor analysis has to be able to recognize one situation out of a set of possibilities. To do so, it uses measurements of more or less doubtful origin and prior knowledge that is understood to be often poorly defined, and whose validity is moreover difficult to evaluate under real observation conditions. The present synthesis proposes a generic modeling of this type of information, in the form of mass sets of the theory of evidence, with closer attention being paid to the most common case where the data originates from statistical processes. On the one hand robust target classification procedures can be achieved by applying appropriate decision criteria to these mass sets, on the other hand they can be integrated rigorously into a target tracking process, to reflect the origin of the localization measurements better.


False Alarm Probability Target Classification Evidence Theory Resolution Cell Focal Element 
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  1. 1.
    A. Appriou: Uncertain data aggregation in classification and tracking processes. In “Aggregation of evidence under fuzziness”, Studies in Fuzziness, Physica Verlag, 1997.Google Scholar
  2. 2.
    A. Appriou: Classification par fusion de données incertaines multi-senseurs. In “Multisensor multitarget data fusion, tracking and identification techniques for guidance and control applications”, AGARDOGRAPH 337, October 1996.Google Scholar
  3. 3.
    A. Appriou: Probabilités et incertitude en fusion de données multi-senseurs. Revue Scientifique et Technique de la Défense, no11, 1991-1, pp 27–40.Google Scholar
  4. 4.
    G. Shafer: A mathematical theory of evidence. Princeton University Press, Princeton, New Jersey, 1976.Google Scholar
  5. 5.
    M.C. Perron-Gitton: Apport d'une approche neuro-floue dans un contexte de fusion de données basé sur la théorie de l'évidence. IPMU' 94, Paris, 4–8 juillet 1994.Google Scholar
  6. 6.
    A. Appriou: Multiple signal tracking processes. Aerospace Science and Technology, no 2, February 1997.Google Scholar
  7. 7.
    Y. Bar Shalom, T.E. Fortmann: Tracking and data association. Academic Press, New York, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Alain Appriou
    • 1
  1. 1.ONERAChâtillon CedexFrance

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