Sensor fusion for situation awareness in littoral environments
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1275 Kinnear Road, Columbus, OH, 43212
Abstract"To maximize situational awareness, while minimizing cognitive overload, automated abductive inference (best-explanation reasoning) will be used to create a changing, "best interpretation" representation of the situation from incoming data. Modeling andsimulation will be used by the abductive inference software for automatic generation of predictions from hypotheses, enabling the continual generation of predictions to support: hypothesis evaluation, sensor tasking, planning, and detection of anomaliesthat may be valuable indications of deception, modeling errors, or sensor failure. Abductive inference will work tightly with predictive infererence to to provide a reliable, self-correcting representation of the situation, based on current evidence fromsensor data, using domain knowledge encoded as causal-model fragments.Aetion proposes to extend its current technology base to create software for building and composing sensor-fusion applications that are modular and extensible as new types of sensors are integrated, and as new knowledge is available about object types,sensor characteristics, and causal processes that mediate the effects of objects on sensors. If this is feasible, the resulting software should be cost effective and highly valuable for multiple sensor fusion applications, resulting in systems able tosqueeze more usable information from less data than systems not using causal relationships and domain models. Our automated i
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