Fusion of uncertain combat identity declarations and implication rules using the belief function theory

摘要

Surveillance systems typically perform target identification by fusing target ID declarations supplied by individual sensors with a prior knowledge-base. Target ID declarations are usually uncertain in the sense that: (1) their associated confidence factor is less than unity; (2) they are non-specific (the true hypothesis belongs to a subset A of the universe Θ). Prior knowledge is typically represented by a set of possibly uncertain implication rules. An example of such a rule is: if the target is Boeing 737 than it is neutral or friendly with probability 0.8. The uncertainty again manifests itself here in two ways: the rule holds only with a certain probability (typically less than 1.0) and the rule is non-specific (neutral or friendly). The paper describes how the fusion of ID declarations and the implication rules can be handled elegantly within the framework of the belief function theory as understood by the transferable belief model (TBM). Two illustrative examples are worked out in details in order to clarify the theory.