Hostname: page-component-cb9f654ff-hqlzj Total loading time: 0 Render date: 2025-08-20T08:16:09.269Z Has data issue: false hasContentIssue false
  • English
  • Français

A 4D ATM Trajectory Concept Integrating GNSS and FMS?

Published online by Cambridge University Press:  14 March 2014

Peter Brooker
Get access Rights & Permissions [Opens in a new window]

Abstract

NextGen and SESAR have now been under development for several years, but have increasingly complex engineering and operational specifications. A variant Air Traffic Management (ATM) concept is sketched for generating fuel-efficient, very accurate and air-ground synchronized 4D-trajectories by using flight segment groundspeed profiles and linking Global Navigation Satellite Systems (GNSS) data to the aircraft Flight Management Systems (FMS) with feedback control. Is this a flawed concept or a feasible and operationally practical proposition?

Keywords

SESARNextGenAir SafetyGNSSFMSATC

Information

Type
Research Article
Information
The Journal of Navigation , Volume 67 , Issue 4 , July 2014 , pp. 617 - 631
Copyright
Copyright © The Royal Institute of Navigation 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Åström, K.J. and Murray, R.M. (2013). Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press.Google Scholar
Bubalo, B. (2011). Airport Punctuality, Congestion and Delay: The Scope for Benchmarking. Aerlines Magazine, 50, 1–9.Google Scholar
Brooker, P. (2009). Simple Models for Airport Delays during Transition to a Trajectory-Based Air Traffic System. Journal of Navigation, 62, 555–570.CrossRefGoogle Scholar
Brooker, P. (2011). Air Traffic Control Separation Minima: Part 1 – The Current Stasis; Part 2 – Transition to a Trajectory-based System. Journal of Navigation. 64(3), 449–465; 64(4), 673–693.CrossRefGoogle Scholar
Brooker, P. (2012). 4D-Trajectory Air Traffic Management: Are There 'Killer Apps?’ Parts 1 and 2. Journal of Navigation, 65(3), 397–408; 65(4), 571–587.CrossRefGoogle Scholar
CASA Australia. (2010). Performance Based Navigation (PBN) Operational Approval Handbook. CASA Australia/ICAO/COSCAP.Google Scholar
Casado, E. (2012). Literature Review: Application of the Theory of Formal Languages to the Modeling of Trajectory Uncertainty and the Analysis of its Impact in Future TBO. SESAR Research Network Towards Higher Levels of Automation in ATM.Google Scholar
Delgado, L. and Prats, X. (2009). Fuel consumption assessment for speed reduction solutions during the cruise phase. In Proceedings of the Conference on Air Traffic Management Economics, Belgrade.Google Scholar
ERASMUS. (2009). Final report. En Route Air Traffic Soft Management Ultimate System. Deliverable WP4 – D 4.6.Google Scholar
Eurocontrol. (2013). Base of Aircraft Data (BADA). http://www.eurocontrol.int/services/bada.Google Scholar
Fistas, N. (2011). Future Aeronautical Communications: The Data Link Component. Future Aeronautical Communications, Ed. Dr. Simon Plass. InTech Europe.CrossRefGoogle Scholar
Garteur. (1990). Final Report of the GARTEUR Action Group FM (AG) 03, Royal Aerospace Establishment, Bedford.Google Scholar
HALA!. (2012). Position Paper: State of the Art and Research Agenda. Edition 2.04. SESAR Research Network Towards Higher Levels of Automation in ATM.Google Scholar
Herndon, A.A. (2012). Flight Management Computer (FMC) Navigation Database Capacity. MITRE Corporation, Center for Advanced Aviation System Development.CrossRefGoogle Scholar
Honeywell International Inc. (2012). Systems and methods for RTA control of multi-segment flight plans with smooth transitions, Patent Application number 12/875,371.Google Scholar
ICAO. (2008). Performance-based Navigation (PBN) Manual. Doc 9613. ICAO.Google Scholar
Jackson, M., Gonda, J., Mead, R. and Saccone, G. (2013). 4D Trajectory Data Link Service – Closing the Loop for Air Traffic Control. Honeywell Aerospace.Google Scholar
JPDO [Joint Planning and Development Office]. (2005). Weather Concept of Operations. Version 1.0. Weather Integrated Product Team, JPDO.Google Scholar
JPDO. (2011). JPDO Trajectory-Based Operations (TBO) Study Team Report. JPDO.Google Scholar
Krozel, J. (2011). Summary of Weather-ATM Integration Technology. Second Aviation, Range and Aerospace Meteorology Special Symposium on Weather-Air Traffic Management Integration. 91st American Meteorological Society Annual Meeting.Google Scholar
Lee, A.G., Weygandt, S.S., Schwartz, B. and Murphy, J. R. (2009). Performance of trajectory models with wind uncertainty. AIAA Modeling and Simulation Technologies Conference, Chicago, Illinois.CrossRefGoogle Scholar
Mondoloni, S. and Kirk, D. (2013). Comparison of Transatlantic Trajectory Activities: FIXM Implications. MP130256. MITRE Center for Advanced Aviation System Development.Google Scholar
Mondoloni, S. and Swierstra, S. (2005). Commonality in Disparate Trajectory Predictors for Air Traffic Management Applications. 24th Digital Avionics Systems Conference.Google Scholar
Nakamura, D. (2009). Aircraft Considerations: JPDO NextGen Trajectory Based Operations (TBO) Conference.Google Scholar
Paglione, M., Bayraktutar, I., McDonald, G.N., and Bronsvoort, J. (2010). Lateral Intent Error's Impact on Aircraft Prediction. Air Traffic Control Quarterly. 18(1) 29–62.CrossRefGoogle Scholar
Paielli, R.A. (2005). Trajectory Specification for High-Capacity Air Traffic Control. Journal of Aerospace Computing, Information, and Communication. 2(9), 361–385.CrossRefGoogle Scholar
Prevot, T., Homola, J.R., Martin, L.H., Mercer, J.S. and Cabrall, D.D. (2012). Toward Automated Air Traffic Control – Investigating a Fundamental Paradigm Shift in Human/Systems Interaction. International Journal of Human-Computer Interaction. 28(2), 77–98.CrossRefGoogle Scholar
REACT Consortium (2008). Preliminary Requirements over a Future AIDL. https://extranet.eurocontrol.int/http://extraweb.eurocontrol.int/corporate/public/event/080624_25_react.html.Google Scholar
Reynolds, T.G. and Hansman, R.J. (2003). Investigating Conformance Monitoring Issues in Air Traffic Control Using Fault Detection Approaches. ICAT-2003-5. International Center for Air Transportation, Massachusetts Institute of Technology.Google Scholar
Sherry, L., Wang, Z., Kourdali, H. and Shortle, J. (2013). Big Data Analysis of Irregular Operations: Aborted Approaches and their Underlying Factors. 6th Integrated Communications Navigation and Surveillance Conference.CrossRefGoogle Scholar
TITAN [Turnaround integration in trajectory and network] (2010). Analysis of the current situation, Version 1.0. Project co-funded by the European Commission and TITAN consortium. http://www.transport-research.info/Upload/Documents/201204/20120405_235823_8638_wp1_slo_del_01_v1.pdfGoogle Scholar
van Gool, M., Schröter, H. (1999). PHARE Final Report. Eurocontrol, Brussels.Google Scholar
Vivona, R.A., Cate, K.T. and Green, S.M. (2011). Comparison of Aircraft Trajectory Predictor Capabilities and Impacts on Automation Interoperability. 11th AIAA Aviation Technology, Integration and Operations (ATIO) Conference.Google Scholar
Walter, R. (2001). Flight Management Systems. Chapter 15 of The Avionics Handbook. (edited by Cary R. Spitzer). CRC Press, Boca Raton.Google Scholar
Welch, J.D., Andrews, J.W., Martin, B.D. and Sridhar, B. (2007). Macroscopic workload model for estimating en route sector capacity. 7th USA/Europe Air Traffic Management R&D Seminar.Google Scholar
Wichman, K.D., Lindberg, L.G.V. and Kilchert, L. and Bleeker, O.F. (2003). Europe's Emerging Trajectory-Based ATM Environment. 22nd Digital Avionics Systems Conference, Indianapolis, USA.Google Scholar
Wilson, I.A.B. (1996). PHARE: Definition and Use of Tubes. Eurocontrol DOC 96-70-18, Eurocontrol.Google Scholar