Belief function divergence as a classifier

A technique is presented for classifying observations of the environment that can be expressed as aggregates of disparate belief functions. The belief functions reflect a level of precision consistent with the current operational status of the sensor suite and the occlusions present in the environment. The classification process consists of applying a divergence measure to the evidential aggregate of belief functions and a set of prototype aggregate belief functions in a knowledge base. Divergence measures the difference between the information present in the combination of the two belief functions and each of the constituents alone. The measure of divergence addresses both the similarity between the two belief functions and their levels of support, thus incorporating both aspects of precision, namely, specificity and certainty. The knowledge base consists of a inference classes, each consisting of a set of aggregate prototypes typifying the class to a degree expressed by a fuzzy epitome coefficient