In order to improve the accuracy of flight trajectory prediction in the terminal area and achieve the goal of short-term conflict warning between two aircraft, a 4D flight trajectory prediction methodology in the terminal area based on twin support vector regression was established. Firstly, the resampling algorithm was applied to the raw flight trajectory to reduce the scale of trajectory dataset. Mercator projection was used to convert the longitude, latitude and height of trajectory point into x-y-z coordinates. Then, the twin support vector regression approach was contributed to construct the prediction model to achieve the goal of aircraft flight trajectory dynamic prediction in short-term. By calculating the horizontal and vertical distance between two aircraft, an aircraft conflict warning indicator function was established. The influence of each hyperparameter on the prediction effect was analyzed through hyperparameter sensitivity analysis. Simulation experiments based on history trajectory in an airport prove that the 4D flight trajectory prediction model based on twin support vector regression can accurately catch the trend of aircraft movement and have robust generalization ability. The root mean square error of x-y-z coordinates of the proposed model is 32%, 35% and 61% of the back-propagation neural network, and the calculation time for a single prediction is reduced by about 0.13 s.
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丁松滨,管吉晨,刘计民. 终端区4D飞行轨迹预测与冲突预警[J]. 科学技术与工程, 2021, 21(28): 12307-12313. Ding Songbin, Guan Jichen, Liu Jimin.4D Flight Trajectory Prediction and Conflict Warning in Terminal Area[J]. Science Technology and Engineering,2021,21(28):12307-12313.