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http://dx.doi.org/10.12985/ksaa.2019.27.4.027

En-route Ground Speed Prediction and Posterior Inference Using Generative Model  

Paek, Hyunjin (한국항공대학교 항공교통물류학과)
Lee, Keumjin (한국항공대학교 항공교통물류학과)
Publication Information
Journal of the Korean Society for Aviation and Aeronautics / v.27, no.4, 2019 , pp. 27-36 More about this Journal
Abstract
An accurate trajectory prediction is a key to the safe and efficient operations of aircraft. One way to improve trajectory prediction accuracy is to develop a model for aircraft ground speed prediction. This paper proposes a generative model for posterior aircraft ground speed prediction. The proposed method fits the Gaussian Mixture Model(GMM) to historical data of aircraft speed, and then the model is used to generates probabilistic speed profile of the aircraft. The performances of the proposed method are demonstrated with real traffic data in Incheon Flight Information Region(FIR).
Keywords
Ground Speed Prediction; Departure Manager; Gaussian Mixture Model; Generative Model; Unsupervised Learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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