Paper Abstract and Keywords |
Presentation |
2022-01-18 11:30
Improvements of Trajectory Estimation for Commercial Aircraft by using Gaussian Process Regression
-- Modeling Calibrated Airspeed of Descending Aircraft in Terminal Airspace -- Daichi Toratani (MPAT, ENRI) SANE2021-86 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Trajectory prediction technique for commercial aircraft is an important element of air traffic control. Since the trajectory prediction is calculated based on an aircraft model, the accuracy of the aircraft model has a significant impact on the trajectory prediction accuracy. In a terminal airspace, which is around an airport, each airspace has an unique operation method; hence, the aircraft model, especially the speed profile, differs in each terminal airspace. In this study, a speed profile suitable for a specific terminal airspace is developed by performing regression on actual flight data. Since the speed profile of an aircraft is nonlinear and the appropriate model is not clear, we use Gaussian process regression, which is a nonparametric regression method. The obtained speed profile is evaluated by comparing it with a widely used model of a passenger aircraft. By applying the speed profile to the trajectory prediction, the effect of the improvement of the speed profile on the accuracy of the trajectory prediction will be evaluated. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Air Traffic Management (ATM) / Trajectory Estimation / Gaussian Process Regression (GPR) / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 323, SANE2021-86, pp. 19-24, Jan. 2022. |
Paper # |
SANE2021-86 |
Date of Issue |
2022-01-11 (SANE) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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SANE2021-86 |
Conference Information |
Committee |
SANE |
Conference Date |
2022-01-18 - 2022-01-19 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
ENRI |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Navigation, air traffic control, and general |
Paper Information |
Registration To |
SANE |
Conference Code |
2022-01-SANE |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Improvements of Trajectory Estimation for Commercial Aircraft by using Gaussian Process Regression |
Sub Title (in English) |
Modeling Calibrated Airspeed of Descending Aircraft in Terminal Airspace |
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Air Traffic Management (ATM) |
Keyword(2) |
Trajectory Estimation |
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Gaussian Process Regression (GPR) |
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1st Author's Name |
Daichi Toratani |
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Electronic Navigation Research Institute, National Institute of Maritime, Port and Aviation Technology (MPAT, ENRI) |
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Speaker |
Author-1 |
Date Time |
2022-01-18 11:30:00 |
Presentation Time |
25 minutes |
Registration for |
SANE |
Paper # |
SANE2021-86 |
Volume (vol) |
vol.121 |
Number (no) |
no.323 |
Page |
pp.19-24 |
#Pages |
6 |
Date of Issue |
2022-01-11 (SANE) |
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