Reconfigurable Bayesian networks for adaptive situation assessment in battlespace

Situation assessment is the task of integrating low-level sensor data to fuse lower level information and produce hypotheses in a military situation. In this paper we propose an algorithm for adaptive situation assessment using 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.