Methods of a Priori Statistical Analysis of Disturbed Motion of Aircraft in Turbulent Environments

The article discusses the methods of a priori statistical analysis used for predicting perturbed motion of aircraft in turbulent environments. Theoretical approaches such as the comparative method and mathematical modeling method are used to analyze the a priori analysis methods. The paper also utilizes statistical methods to evaluate the effectiveness of stochastic models to account for random perturbations caused by turbulence. Special attention is paid to the use of Bayesian analysis, maximum likelihood method and Monte Carlo method applied for probabilistic prediction of the aircraft trajectory. The presented models are illustrated with formulas that describe the dynamics of vehicle motion in turbulent conditions, including equations of motion based on Newton’s and Euler’s laws. The parameters that determine the dynamics of the perturbed motion of the aircraft in a turbulent environment, such as linear and angular velocities, turbulence intensity, aerodynamic forces, moments of inertia and meteorological conditions, are studied to evaluate the correctness of the calculations. This allows the effects of turbulence on the control and flight trajectory of the aircraft to be taken into account. The results of the study demonstrate the high accuracy of the proposed methods in predicting aircraft motion deviations and emphasize the importance of further development of computational approaches to integrate these methods into real-time control systems, especially for application in conditions of uncertainty and complex external influences. Further research could focus on improving the adaptability of models for different types of aircrafts, taking into account the optimization of computational methods to reduce computational complexity. This will make it possible to improve the efficiency of forecasts in a shorter time and reduce resource costs.