Reconfigurable Bayesian Networks for Hierarchical Multi-Stage Situation Assessment in Battlespace

Situation assessment is the task of summarizing low-level sensor data in a battlefield environment to produce hypotheses suitable to use in command and control decision making. In this paper we devise a novel algorithm for adaptive multi-stage situation assessment using a hierarchy of reconfigurable Bayesian networks. The formulation and algorithm presented are suitable for dynamic battlespace situation changes. We provide numerical examples that show the effectiveness of our approach in a battlefield scenario