Automated mode inferencing for intelligent aircraft

In this paper we introduce a novel mode-sensing system for aircraft, called a mission segment identifier (MSI). Using a blend of soft-computing approaches, the MSI infers the current portion of a commanded mission from sensed aircraft state variables. This inference contributes situation awareness to a pilot-like intelligent control system for autonomous aircraft. A two-level architecture for the MSI has been developed using neural networks for the lower level and a fuzzy inference system for the higher level. The resulting MSI has been validated using data from simulated flights of a conceptual autonomous aircraft. The performance of the MSI in the presence of sensor noise and ambiguities has been studied. A new method to synthesize training data for the neural network used in the lower level of the MSI evolved as part of this exercise. A simple, yet effective, scheme to reduce uncertainty in the identification has also been developed and validated