Target Identification Based on the Transferable Belief Model Interpretation of Dempster-Shafer Model. Pars I: Methodology

This paper explains how multisensor data fusion and target identification can be performed within the transferable belief model, a model for the representation of quantified uncertainty based on belief functions. The paper is presented in two parts: methodology and application. In this part, we present the underlying theory, in particular the General Bayesian Theorem needed to transform likelihoods into beliefs and the pignistic transformation needed to build the probability measure required for decision making. We end with a simple example. More sophisticated examples and some comparative studies are presented in Part II. The results presented here can be extended directly to many problems of data fusion and diagnosis.