Combining Model-based Reasoning with Knowledge Discovery Techniques for Level 2 and 3 Fusion Navy STTR FY2005 Sol No.: Navy STTR FY2005 Topic No.: N05-T019 Topic Title: Combining Model-based Reasoning with Knowledge Discovery Techniques for Level 2 and 3 Fusion Proposal No.: N054-019-0140 Firm: Charles River Analytics Inc. 625 Mount Auburn Street Cambridge, Massachusetts 02138-4555 Contact: Subrata Das Phone: (617) 491-3474 Web Site: www.cra.com Abstract: We propose to develop an approach to combine model based reasoning with knowledge discovery techniques for enhanced Level 2 and 3 data fusion, especially suitable for detecting asymmetric threats (e.g. ambush, insurgency) in cluttered urban environments. The knowledge discovery part: 1) deploys evidence filtering of large volumes of intelligence data to detect low-signature significant spatio-temporal events; and 2) uses clustering to perform spatial and time-series analysis of messages without requiring semantic information in the data. The former, for example, detects and tracks isolated suspicious vehicles, whereas the latter detects spatially correlated moving units over time within urban environments. Detected events and patterns trigger the need for assessing newly developed situations and threats, resulting in invocations of doctrine-based static and dynamic Bayesian belief network (BN) models that are causal and graphical in nature, and are well known for handling uncertainty. The selected BN models then perform higher-level data fusion based on other observables propagated as evidence into the models, by taking into account varying credibility and confidence of information sources via the Dempster-Shafer (D-S) theory of belief functions. The proposed hybrid approach will significantly enhance the fusion capability of DCGS-MC and C2PC for Marine Corps operations in urban environments. Benefits: Commercial applications of the proposed approach to situation and threat assessment exist for a wide variety of contexts characterized by high rates of information flow from sensors within an environment to be monitored. Such application areas include operation centers for complex process control (e.g., nuclear power plants, water resource distribution), road traffic management centers, and commercial surveillance centers for law enforcement. The dual use of the developed strategy for handling reliability and confidence of information sources is for evidence preparation to be embedded within our commercial belief network engine. Return