Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-10T21:08:26.420Z Has data issue: false hasContentIssue false

Performance Evaluation of a Novel 4D Trajectory Prediction Model for Civil Aircraft

Published online by Cambridge University Press:  26 June 2008

Marco Porretta*
Affiliation:
(Imperial College London)
Marie-Dominique Dupuy
Affiliation:
(Imperial College London)
Wolfgang Schuster
Affiliation:
(Imperial College London)
Arnab Majumdar
Affiliation:
(Imperial College London)
Washington Ochieng
Affiliation:
(Imperial College London)

Abstract

Future air traffic management will require a variety of automated decision support tools to provide conflict-free trajectories and their associated error margins. The ability to correctly forecast aircraft trajectories, i.e. trajectory prediction, is the central component of such automated tools, which will enable continued provision of safe and efficient services in increasingly congested skies. Current approaches for trajectory prediction, available in the open literature, make a number of assumptions in order to simplify the mathematical models of aircraft motion. Furthermore, many existing methods perform three-dimensional trajectory prediction, in which information on expected times of arrival at significant points along the intended aircraft route is not considered. This results in inaccurate trajectories not suitable for conflict detection and resolution. This paper presents a novel four-dimensional trajectory prediction scheme that makes full use of data on expected times of arrival. A three dimensional point-mass model for a standard civil aircraft is used to emulate aircraft dynamics, while the aircraft operating mode is characterised through a set of discrete variables. The aircraft performance model used relies on the EUROCONTROL Base of Aircraft Data (BADA) set and the computed trajectory accounts for the effects of wind. Inputs include navigation data and aircraft intent information, which unambiguously define the trajectory to be computed according to the flight plan. In the proposed model, aircraft intent information is summarised in a simple, but effective, set of instructions contained in a Flight Script. Furthermore, two key innovations to trajectory prediction are introduced. Firstly, a novel scheme to emulate the control system used for aircraft lateral guidance is proposed and secondly, on the basis of aircraft intent information, a new procedure to estimate speed is presented. The performance of the enhanced trajectory model proposed is quantified using a detailed operational dataset (real flight data) captured in a European airspace. The results show that, over an extended time-horizon, the enhanced model is more accurate than two representative existing methods, and that it is suitable for reliable trajectory prediction.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2008

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.)

References

REFERENCES

Beers, C. S. and Huisman, H. (2002). Transitions between Free Flight Airspace and Managed Airspace, A controllers' perspective. NLR-TP-2002-433, National Aerospace Laboratory, NLR.Google Scholar
Casaux, F. and Hasquenhoph, B. (1997). The operational use of ASAS. Paper presented at the 1 stUSA/EUROPE Air Traffic Management R&D Seminar, Paris, France.Google Scholar
EUROCONTROL (1987). Aircraft Modelling Standards for Future Air Traffic Control Systems. EUROCONTROL Division E1, document No. 872003.Google Scholar
EUROCONTROL (1998). Air Traffic Management Strategy for 2000+. Volumes 1&2.Google Scholar
EUROCONTROL Experimental Centre (2004). User Manual for the Base of Aircraft Data (BADA). Revision 3.6, EEC Note 10/04.Google Scholar
EUROCONTROL (2007a). First ATC Support Tools Implementation (FASTI) – Operational Concept, “FASTI” Programme.Google Scholar
EUROCONTROL (2007b). The ATM Target Concept. Document No. DLM-0612-001-02-00. Deliverable D3, “SESAR” Programme.Google Scholar
Glover, W. and Lygeros, J. (2003). A Multi-Aircraft Model for Conflict Detection and Resolution Algorithm Validation. Technical Report WP1, Deliverable D1.3, “HYBRIDGE” Project.Google Scholar
Glover, W. and Lygeros, J. (2004). A Stochastic Hybrid model for Air Traffic Control Simulation. Hybrid Systems: Computation and Control, ser. LNCS, R. Alur and G. Pappas, Eds. Springer Verlag, 2993, 372386.Google Scholar
Honeywell (1989). B747-400 Flight Management System Guide. Honeywell Inc. Phoenix, Arizona, USA.Google Scholar
ICAO (1964) Manual of the ICAO Standard Atmosphere,” ICAO document No. 7488, 2nd Edition.Google Scholar
Kuchar, J. and Yang, L. C. (2000). A review of Conflict Detection and Resolution Methods. IEEE Transactions on Intelligent Transportation Systems, 1, 4, 179189.CrossRefGoogle Scholar
Majumdar, A. and Ochieng, W. Y. (2002). The factors affecting air traffic controller workload: a multivariate analysis based upon simulation modelling of controller workload. Transportation Research Record, 1788, 5869.CrossRefGoogle Scholar
Oppenheim, A. V. and Schafer, R. W. (1975). Digital Signal Processing. Prentice Hall, Englewood Cliffs NJ.Google Scholar
Slattery, R. and Zhao, Y. (1996). Capture Conditions for Merging Trajectory Segments to Model Realistic Aircraft Descents. Journal of Guidance, Control, and Dynamics, 19, 2, 453460.Google Scholar
Slattery, R. and Zhao, Y. (1997). Trajectory Synthesis for Air Traffic Automation. Journal of Guidance, Control, and Dynamics, 20, 2, 232238.CrossRefGoogle Scholar
Vilaplana Ruiz, M. A. (2005). COURAGE Domain Analysis. Deliverables D2.1 and D3.1. The Boeing Research & Technology Europe, Madrid, Spain.Google Scholar