Abstract
A fault-tolerant neural network algorithm was successfully developed for use with flush air data sensing systems. This algorithm is composed of a combination of aerodynamic and neural network models used to translate a discrete pressure distribution from the nose of an aircraft into a set of air data parameters, including static pressure, dynamic pressure, Mach number, angle of attack, and angle of sideslip. Techniques were developed to detect and eliminate the effect of a lost signal from the measured pressure distribution. This system was evaluated with archived data, and its performance was compared with a signal processing system based completely on aerodynamic models
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