Critiques on some combination rules for probability theory based on optimization techniques

A crucial point in the decision-level identity fusion is to combine information in an appropriate way to generate an optimal decision, according to the individual information coming from a set of different sensors. An interesting approach was developed for the decision- level identity fusion, which use optimization techniques to minimize an objective function which measure the dissimilarities between the combination result and the set of initial sensor reports. Several objective functions were already proposed for the similar sensor fusion (SSF) and the dissimilar sensor fusion (DSF) models. In this paper, we present these fusion methods, we raise some questions and make some improvements, and finally we study the behaviour of these fusion rules on several examples.