Abstract: This paper describes a fundamentally new theoretical approach to data fusion based on a novel type of random variable called the random finite set, and on a generalization of the familiar radon-nikodym derivative from the theory of the Lebesgue integral. We have shown how to directly generalize classical (i.e., single-sensor, single-target) parametric point estimation theory to the multi-sensor, multi-target, localization and classification realm. Using this theory we have shown that it is possible to construct data fusion algorithms in which detection, correlation, tracking and classification are unified into a single probabilistic procedure. We have also shown that a Cramer-Rao inequality holds for a general class of data fusion algorithms, apparently the first ever. !23
展开▼
