A Dynamic Data-Driven Approach to Optimal Offline Learning for Online Flight Capability Estimation

In this paper a data-driven approach to producing rapid, online estimates of aircraft capability by associating offline learned scenarios with the current aircraft state is presented. This association is carried out via an online Bayesian classification process that dynamically incorporates online sensed data by incorporating a novel confidence mapping approach that enables rapid online quantification of uncertainty in capability estimates. The problem of optimal offline learning for library construction for given online stochastic mission scenarios is formulated and a demonstration of an exhaustive search solution, as well as a greedy approach is presented. Our methodology and demonstrations are developed in the context of a high altitude, long endurance unmanned aerial vehicle.