Adaptive battlespace situation assessment using a hierarchy of reconfigurable Bayesian networks

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. In this thesis a novel algorithm is devised for adaptive multi-stage situation assessment using a hierarchy of Bayesian networks that are reconfigured on two timescales. Network Centric Warfare concepts are used in designing the situation assessment system. The formulation and algorithms presented are suitable for dynamic battlespace situation changes. Furthermore, algorithms are provided to model the battlespace conditions as a stochastic feedback system that uses the hypotheses generated by the Bayesian networks to make decisions. Numerical examples are provided to demonstrate the effectiveness of these algorithms.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat