Blue Bear (United Kingdom)

Flush air data sensing systems have been widely applied to large (manned) aircraft, where pressure orifices are typically located at the nosetip. This paper investigates the feasibility of a flush air data sensing system designed to estimate the air data states of a small unmanned air vehicle flown at speeds as low as Mach 0.07. Furthermore, due to the presence of a nose propeller, the pressure orifices are located at the wing leading edge. The motivation behind this project is the fact that traditional air data booms are physically impractical for small unmanned air vehicles. Overall, an 80 and 97% reduction in instrumentation weight and cost, respectively, were achieved. Both parametric and multilayer perceptron neural network models have been previously applied in the literature to model the aerodynamic relationship between aircraft surface pressure and the air data states. In this paper, an extended minimum resource allocating network radial basis function neural network is used as the flush air data sensing system model, due to its good generalization capabilities and compact structure. Computational fluid dynamic simulations are implemented to identify the ideal pressure port locations, and wind-tunnel tests are carried out to train and test the extended minimum resource allocating network radial basis function neural network.

For Unmanned Air Vehicles to be successfully deployed in strike missions, system intelligence must be increased to robustly replace the planning, decision-making and conflict resolution functionality normally carried out by the crew in similarly deployed strike aircraft. The process of accurately capturing the requirements for such vehicles depends on evolutions from current manned aircraft operation thinking and synthetic environment based experimentation. The process is also used for technical development and refinement of intelligent machine based code and accordingly requires a significant investment in the fidelity of the synthetic environment. This work addresses the feasibility of capitalizing on the investment in the synthetic environment and using it to reduce the lengthy knowledge acquisition process. By using machine learning capability the aim is to reduce the length of the total knowledge acquisition and code development process. Initial experimentation with both deductive and inductive learning techniques is described with results presented. The paper concludes by postulating the likely benefits of such an approach as well as discussing the evident limitations. I. Nomenclature