Distributed Relational Modeling for Counterspace Situation Awareness

CleverSet proposes to apply the variable-resolution Bayesian technology developed in Phase I to distributed counterspace situation awareness. Variable-resolution modeling provides the proven effectiveness of Bayesian data fusion in an efficient, scalable, distributed architecture. A recent breakthrough, relational Bayesian networks, provides an underlying entity-relationship data model, providing a principled foundation for the higher levels of fusion needed for situation awareness. The results of the proposed work will be a concrete architecture for scalable, distributed data fusion, together with a flyable instantiation of the architecture and a set of studies evaluating its performance. The capabilities included in the final prototype will include: 1. Detection, association, tracking, and assessment of entities, relationships, and overall situations (the delivered capabilities will be optimized for level 2 fusion and above). 2. Multiple-hypothesis relationship and situation modeling. 3. Distributed situation modeling, robust to single-point of failure in both processing nodes and communication links. 4. Automated management of node processing and storage utilization, as well as link bandwidth utilization. 5. An initial set of counterspace situation models. 6. A simple user interface providing inspection capability into the distributed situation model.