Grammatical methods for situation and threat analysis

The needs of command and control (C2) dominate the requirements on data fusion technology. We analyze cognitive and technical models of data fusion and C2 and propose a new functional model that unifies and extends the JDL fusion model and Bryant´s CECA cognitive model. This model clarifies the requirement for human-controllable coherent inferencing of situation and course of action, both retrospectively and predictively. It in turn motivates a common representation for situations, threats, impacts and courses of action. We show that the situation tree formalism has the required representational capacity. We define its semantics, establish its decidability and soundness and show how it can be applied to land and air scenarios (the Battle of Alamein and a combat air patrol). A situation tree can be extracted from a sequence-set combinatory categorial grammar (SSCCG) tree, which can be estimated from sensor data. A fast agenda-based parsing algorithm is presented. Results show that even batch parsing is able to provide optimal situation tree estimation for realistically sized situations in under thirty seconds. The average tagging accuracy on test data was 97.79%.