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A 4D Trajectory Prediction Model Based on the BP Neural Network

Authors
  • Wu, Zhi-Jun1
  • Tian, Shan1
  • Ma, Lan2
  • 1 College of Electronic Information and Automation, Civil Aviation University of China, China , (China)
  • 2 College of Air Traffic Management, Civil Aviation University of China, China , (China)
Type
Published Article
Journal
Journal of Intelligent Systems
Publisher
De Gruyter
Publication Date
Aug 21, 2019
Volume
29
Issue
1
Pages
1545–1557
Identifiers
DOI: 10.1515/jisys-2019-0077
Source
De Gruyter
Keywords
License
Green

Abstract

To solve the problem that traditional trajectory prediction methods cannot meet the requirements of high-precision, multi-dimensional and real-time prediction, a 4D trajectory prediction model based on the backpropagation (BP) neural network was studied. First, the hierarchical clustering algorithm and the k-means clustering algorithm were adopted to analyze the total flight time. Then, cubic spline interpolation was used to interpolate the flight position to extract the main trajectory feature. The 4D trajectory prediction model was based on the BP neural network. It was trained by Automatic Dependent Surveillance – Broadcast trajectory from Qingdao to Beijing and used to predict the flight trajectory at future moments. In this paper, the model is evaluated by the common measurement index such as maximum absolute error, mean absolute error and root mean square error. It also gives an analysis and comparison of the predicted over-point time, the predicted over-point altitude, the actual over-point time and the actual over-point altitude. The results indicate that the predicted 4D trajectory is close to the real flight data, and the time error at the crossing point is no more than 1 min and the altitude error at the crossing point is no more than 50 m, which is of high accuracy.

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