Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-18T20:27:16.827Z Has data issue: false hasContentIssue false
  • English
  • Français

Trajectory prediction for future air traffic management – complex manoeuvres and taxiing

Part of: The RAeS Written Paper Prizes 2016

Published online by Cambridge University Press:  27 January 2016

W. Schuster
W. Schuster*
Affiliation:
Centre for Transport Studies, Imperial College, London, UK
*
w.schuster@imperial.ac.uk
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The future air traffic management (ATM) concept envisaged by the Single European Sky ATM Research – SESAR – and the USA equivalent NextGen, mark a paradigm shift from the current reactive approach of ATM towards holistic strategic collaborative decision making. The core of the future ATM concept relies on common situational awareness over potentially large time-horizons, based upon the user operational intent. This is beyond human capabilities and requires the support of automation tools to predict aircraft state throughout the operation and provide support to optimal decision making long before any potential conflict may arise. This is achieved with trajectory predictors and conflict detectors and resolvers respectively. Numerous tools have been developed, typically geared towards addressing specific airborne applications. However, a comprehensive literature search suggests that none of the tools was designed to predict trajectories throughout the entire operation of an aircraft, i.e. gate-to-gate. Yet, such functionality is relevant in the holistic optimisation of aircraft operations. To address this gap, this paper builds on an existing en route trajectory prediction (TP) model and develops novel techniques to predict aircraft trajectories for the transitions between the ground- and enroute-phases of operation and for the ground-phase, thereby enabling gate-to-gate (or enroute -to-enroute) TP. The model is developed on the basis of Newtonian physics and operational procedures. Real recorded data obtained from a flight data record (FDR) were used to estimate some of the input parameters required by the model. The remaining parameters were taken from the BADA 3.7 model. Performance results using these flight data demonstrate that the proposed TP model has the potential to accurately predict gate-to-gate trajectories and to support future ATM applications such as gate-to-gate synchronisation.

Type
Research Article
Information
The Aeronautical Journal , Volume 119 , Issue 1212 , February 2015 , pp. 121 - 143
Copyright
Copyright © Royal Aeronautical Society 2015

References

1. SJU, The European ATM Master Plan, (second edition), 2012, https://www.atmmasterplan.eu/.Google Scholar
2. FAA, NextGen Implementation Plan, 2013, https://www.faa.gov/nextgen/implementation/media/NextGen_Implementation_Plan_2013.pdf.Google Scholar
3.Musialek, B.et al Literature survey of trajectory predictor technology, 2010, TC-TN11/1.Google Scholar
4.Petricel, B. and Costelloe, C. First ATC support tools implementation (FASTI) operational concept, Edition 1.1, 2007.Google Scholar
5.Paielli, R.Tactical conflict resolution using vertical maneuvers in en route airspace, AIAA J Aircr, 2008, 45, (6).CrossRefGoogle Scholar
6.Erzberger, H. and Heere, K.Algorithm and operational concept for resolving short range conflicts, 2008, 26th International Congress of the Aeronautical Sciences, Anchorage, Alaska, USA.Google Scholar
7.Konyak, M.et alImproving ground-based trajectory prediction through communication of aircraft intent, AIAA Guidance, Navigation and Control, 2009, Chicago, IL, USA.Google Scholar
8.Romanelli, J.et alClimb trajectory prediction software validation for decision support tools and simulation models, ITEA J, 2009, 30, pp 481–491.Google Scholar
9.Marceau, G.et alOnline learning for ground trajectory prediction, 2012, SESAR Innovation Days, Braunschweig.Google Scholar
10.Christien, R. and Pugh, R. Sharing data could improve trajectory prediction, 2012, P5.5.2, http://www.eurocontrol.int/articles/sharing-data-could-improve-trajectory-prediction.Google Scholar
11.Glover, W. and Lygeros, J. A multi-aircraft model for conflict detection and resolution algorithm validation, HYBRIDGE, 2004, WP1 (Deliverable D1.3).Google Scholar
12.Fairley, G. and McGovern, S.A Kinematic/kinetic hybrid airplane simulator model, 2008, ASME International Mechanical Engineering Congress and Exposition, Boston, MA, USA.CrossRefGoogle Scholar
13.Karr, D.et alAutonomous operations planner: A flexible platform for research in flight-deck support for airborne self-separation, 2012, 12th AIAA Aviation Technology, Integration, and Operations (ATIO), Indianapolis, USA.Google Scholar
14.Schuster, W., Porretta, M. and Ochieng, W.High-accuracy four-dimensional trajectory prediction for civil aircraft, Aeronaut J, 2012, 116, (1175).CrossRefGoogle Scholar
15. Eurocontrol and FAA, White Paper – Common TP structure and terminology in support of SESAR & NextGen, Eurocontrol/FAA Action Plan 16 Common Trajectory Prediction Capability, Edition 1.0, 2010.Google Scholar
16.Mondoloni, S. and Kirk, D. Proposed trajectory prediction and exchange information items for flight information exchange model (FIXM), 2012, http://www.fxm.aero/sites/default/fles/dashboardfles/dashboard/gf/MITRE%2012.pdf.Google Scholar
17. Eurocontrol, User Manual for the Base of Aircraft Data (BADA), Version 3.7, 2009.Google Scholar
18. EASA, RuFAB – Runway friction characteristics measurement and aircraft braking, Vol 3, 2008, (EASA.2008/4).Google Scholar
19.Sadraey, M., Drag Force and Drag Coeffcient, 2009, VDM Verlag Dr Mueller.Google Scholar
20. Eurocontrol, Reference Validation Data Base, 2009.Google Scholar
21. Eurocontrol, BADA 4.0, 2014, http://www.eurocontrol.int/services/bada.Google Scholar
22.Poles, D., Nuic, A. and Mouillet, V.Advanced aircraft performance modeling for ATM: Analysis of BADA model capabilities, 2010, Digital Avionics Systems Conference (DASC), Salt-Lake City, UT, USA.Google Scholar
23. Eurocae, MASPS required navigation performance for area navigation, 2012, ED75B (Addendum) www.eurocae.net.Google Scholar