An Adaptive Data Fusion and Analysis System

This project develops an adaptive data fusion and analysis system to intelligently process, analyze, combine, and abstract useful knowledge from heterogeneous test data sources aboard aircraft/ship by integrating evidence theory, fuzzy logic, plausible reasoning, and dynamic probabilistic networks. Evidence theory serves as a rule induction and knowledge extraction process. A fuzzy clustering procedure extracts information from the original data sources. A plausible reasoning module permits the synthesis of knowledge acquired from different sources and performs causal inference processes to enhance decision aid processes. Dynamic probabilistic networks serve to maintain and update the belief state over time, providing update information for optimal decision making. The proposed intelligent information fusion and analysis system not only takes advantage of innovative individual methodologies but also, more significantly, emphasizes the synergism among all subsystems to assure the overall performance of the information fusion and analysis system. The end product is a commercial software package, An Adaptive Data Fusion and Analysis System, which will dramatically increase the productivity of organizations involved in data management, data mining, data fusion and analysis, and structure and pattern extraction from huge databases of test aboard aircraft/ship. The effectiveness of the proposed techniques is demonstrated on a fully integrated prototype module at the end of Phase I.