Situation assessment in urban combat environments

In this paper, a hybrid approach combining model-based reasoning with knowledge discovery techniques for SA is explored, which is suitable for detecting and identifying asymmetric threats in urban environments. The hybrid approach recognizes significant patterns by taking into account environmental clutter. It also uses clustering algorithms to perform a space and time-series analysis of messages without requiring semantic information. Detected patterns trigger follow-up assessment of newly developed situations, resulting in invocations of various doctrine-based computational models, including causal static and dynamic Bayesian belief networks (BNs). The invoked models then perform SA based on other observables propagated as evidence into the models.