Target identification using belief functions and implication rules

Presented here is the theoretical basis of data fusion for the purpose of target identification using the belief function theory. The key feature is that we allow the knowledge sources to supply their information in the form of uncertain implication rules. How these rules can be elegantly handled within the framework of the belief function theory is described. A small scale, practical example for target identification is worked through in detail to clarify the theory for future users.