Aircraft trajectory forecasting using local functional regression in Sobolev space |
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| Institution: | 1. Ecole National de l’Aviation Civile, Département de Mathématique Appliquées, Informatique et Automatique pour l’Aérien, 7, Avenue Edouard Belin, 31055 Toulouse, France;2. Institut National des Sciences Appliquées, Département de Génie Mathématique et Modélisation, 135, Avenue de Rangueil, 31077 Toulouse, France;1. Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid. Avenida de la Universidad, 30, Leganés, 28911 Madrid, Spain;2. MeteoSolutions GmbH., Wilhelminenstrasse 2, D-64283 Darmstadt, Germany;3. Leibniz University of Hannover, Germany;4. Agencia Estatal de Meteorología(AEMET), Valencia, Spain;1. Aeronautical Systems, Air Transport and Airports Department, Universidad Politécnica de Madrid, Madrid, Spain;2. Centre for Aeronautics, School of Aerospace, Transport and Manufacturing. Cranfield University, Cranfield, United Kingdom;3. CRIDA A.I.E., Madrid, Spain;1. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;2. Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong, Shatin, N.T., Hong Kong |
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| Abstract: | This paper considers the problem of short to mid-term aircraft trajectory prediction, that is, the estimation of where an aircraft will be located over a 10–30 min time horizon. Such a problem is central in decision support tools, especially in conflict detection and resolution algorithms. It also appears when an air traffic controller observes traffic on the radar screen and tries to identify convergent aircraft, which may be in conflict in the near future. An innovative approach for aircraft trajectory prediction is presented in this paper. This approach is based on local linear functional regression that considers data preprocessing, localizing and solving linear regression using wavelet decomposition. This algorithm takes into account only past radar tracks, and does not use any physical or aeronautical parameters. This approach has been successfully applied to aircraft trajectories between several airports on the data set that is one year air traffic over France. The method is intrinsic and independent from airspace structure. |
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| Keywords: | Trajectory prediction Functional regression Air Traffic Management |
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