Decision Making Under Uncertainty

The proposed effort will develop a new approach to characterize and analyze multiple-sensor decision making under uncertainty. The major focus will be on the decision architecture involving multiple Ballistic Missile Defense (BMD) elements and the evaluation of the performance of various decision architectures for ballistic missile object discrimination. The proposed method will provide a well-defined performance measure to evaluate the decision making architecture and to analyze the errors that are inherent to sensor and inference systems in various decision architectures. The proposed study will use the hierarchical Bayesian inference method using effective implementation of discrete-continuous-variable Bayesian network inference algorithm developed at George Mason University, Department of Systems Engineering and Operations Research. We will develop information fusion algorithms using different fusion architectures, and evaluate their performance in Phase I. Approved for Public Release 14-MDA-7979 (16 September14).