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Multisensor data fusion in situation assessment processes

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

Abstract

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.

Keywords

False Alarm Probability Target Classification Evidence Theory Resolution Cell Focal Element 
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 1997

Authors and Affiliations

  • Alain Appriou
    • 1
  1. 1.ONERAChâtillon CedexFrance

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