Data Processing in the Pilot Training Process on the Integrated Aircraft Simulator

Abstract

Flight safety is an integral part of air transportation. Flight accidents are highly unlikely to appear but most of them are caused by the human factor. The aircrew training system for abnormal operations relies on integrated aircraft simulator-based exercises. Crew needs to be trained not to degrade piloting technique quality when facing increased psychophysiological tension. Therefore, methods evaluating the characteristics of ergatic aircraft control systems, warning systems for deterioration due to failures in avionics systems, piloting technique quality, and abnormal operation algorithms are necessary. An analysis of the bank angle has revealed that there are hidden increased tension manifestations in the human operator expressed in the transition of the flight parameter variation from a stationary random process to deterministic fluctuations in the form of a sinusoid. The goal of the research is to increase the efficiency of pilots’ training using integrated aircraft simulators based on the design and implementation of statistical data processing algorithms. To achieve the goal of the research, two algorithms for detecting deterministic fluctuations based on the Neyman-Pearson criterion and the optimal Bayesian criterion are developed. The presented algorithms can be used in the integrated simulator software to automate the decision-making process on piloting quality.

Keywords

• aircraft
• algorithm design and analysis
• data analysis
• detection algorithms
• human factors
• random processes
• training