Methods for multisensor classification of airborne targets integrating evidence theory

This paper proposes to analyze methods applied to the multisensor classification of airborne targets and which present the common feature of using the theory of evidence developed by Dempster and Shafer. After briefly outlining this technique, we deal more especially with the following three methods: the global method proposed by G. Shafer, the separable method recommended by A. Appriou and finally the extension of the standard K nearest neighbors method proposed by T. Denoeux. These latter are particularly appropriate in the treatment of multisensor classification problems since they make it possible to consider non-exclusive hypotheses and to be able to manipulate uncertain data. Several simulations relating to an airborne target classification problem are presented. They demonstrate the advantages and drawbacks of the various methods proposed and allow their respective behavior to be studied. The early results obtained show that these methods are particularly robust and perform well provided that certain hypotheses are satisfied. A further avenue of research may consist of validating them on the basis of actual measurements recorded using several different sensors. (C) Elsevier, Paris.