CLASSIFICATION USING SET-VALUED KALMAN FILTERING AND LEVIS DECISION-THEORY

We consider the problem of using Levi's expected epistemic decision theory for classification when the hypotheses are of different informational values, conditioned on convex sets obtained from a set-valued Kalman filter. The background of epistemic utility decision theory with convex probabilities is outlined and a brief introduction to set-valued estimation is given. The decision theory is applied to a classifier in a multiple-target tracking (MTT) scenario. A new probability density, appropriate for classification using the ratio of intensities, is introduced.