A method for flight track prediction

Abstract

The invention discloses a flight track prediction method, which considers the meteorological environment where the flight is located, uses a convolutional neural network to extract meteorological features, uses a cyclic neural network to model the track features, and finally outputs a series of future flights Trajectory, specifically including track data preprocessing, meteorological feature extraction, definition of time series model, model compilation and training, track prediction. Through the technical scheme of the invention, the long-term trajectory of the flight can be accurately predicted in the actual environment.

Description

technical field [0001] The invention belongs to the technical field of civil aviation, and in particular relates to a flight path prediction method. Background technique [0002] The high-quality growth of the national economy has promoted the vigorous development of the air transport industry. According to the "2018 Civil Aviation Industry Development Statistical Bulletin", there will be new downward pressure on the economy in 2018, and the development of the civil aviation industry will still maintain a good momentum of steady progress. . It can be seen from the statistical data that my country's air traffic flow is increasing, the coverage of air routes is becoming wider, and the distribution of airports is becoming more dense. However, limited airspace resources, coupled with severe weather disturbances, large-scale airspace congestion and large-scale flight delays have become increasingly prominent. Therefore, in order to ensure the safety of airspace operations and i...

AI Technical Summary

Problems solved by technology

Deterministic methods include optimal estimation and dynamics or kinematics modeling, the limitation of which is that it does not consider any uncertainties, such as convective weather effects, etc., and the prediction accuracy is low

The probabilistic method is mainly the machine learning model. Although the traditional machine learning model has achieved good performance in data mining and prediction, there are still the following deficiencies in track prediction: First, the existing methods lack consideration of dynamic meteorological impacts. Second, it is difficult for existing methods to predict medium and long-term flight paths; Third, due to the numerous characteristics of flight trajectories and strong time series correlation, it shows complex nonlinear characteristics, and it is difficult for traditional machine learning methods to predict them. accurate characterization

In summary, the existing methods are not effective in actual long-term trajectory prediction

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