Multi-hypothesis database for large-scale data fusion

Progress in deploying large-scale fusion systems has been slow in recent years despite substantial algorithmic developments. One reason is that there has not been a way to address a large-scale enterprise in a tractable manner that allows modular and collaborative evolution of fusion algorithms. Information and data modeling techniques have become quite mature over the past 20 years so that it is now possible to model the information domain of a large-scale enterprise tractably. By extending information modeling constructs to semantic and inference nets, it is possible to use these information models as a basis for large-scale fusion. This paper shows how to instrument an information model into a fusion inference structure. Algorithms encapsulation and computing techniques are discussed This approach could lead to foundations for large-scale fusion in defense, intelligence, law enforcement, and air traffic control systems.