An Approach of ACARS Trajectory Reconstruction Based on Adaptive Cubic Spline Interpolation

  • Lan Ma
  • Shan Tian
  • Yang Song
  • Zhijun WuEmail author
  • Meng Yue
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11637)


Trajectory reconstruction is one of the key technologies to achieve flight trajectory and ensure the safety of flight. Aircraft Communication Addressing and Reporting System (ACARS) is a digital data link system that transmits short messages by radio or satellite between aircraft and ground station. In this paper, an approach based on adaptive cubic spline interpolation is proposed for ACARS trajectory reconstruction. The ACARS data points of different flight phases are reconstructed, and the appropriate trajectory curve is obtained. This approach is verified in simulation platform by using true flight historical data. Experimental results show that this approach obtained better smoothness and lower error precision than that of traditional trajectory reconstruction algorithm, especially in take-off and landing phases. Improving the degree of cure smoothing and decreasing its error are helpful to the accurate trajectory and position of the flight, which provides a guarantee for the safe operation of the air traffic.


Flight safety ACARS Trajectory reconstruction Adaptive Cubic spline 


  1. 1.
    Andrienko, G., Andrienko, N., Fuchs, G., Garcia, J.M.C.: Clustering trajectories by relevant parts for air traffic analysis. IEEE Trans. Vis. Comput. Graph. 24(1), 34–44 (2018)CrossRefGoogle Scholar
  2. 2.
    Hong, Y., Choi, B., Lee, K., Kim, Y.: Conflict management considering a smooth transition of aircraft into adjacent airspace. IEEE Trans. Intell. Transp. Syst. 17(9), 2490–2501 (2016)CrossRefGoogle Scholar
  3. 3.
    Jackson, M.R.C.: Role of avionics in trajectory-based operations. IEEE Aerosp. Electron. Syst. Mag. 25(7), 12–19 (2010)CrossRefGoogle Scholar
  4. 4.
    Ellerbroek, J., Visser, M., van Dam, S.B.J., Mulder, M., van Paassen, M.M.: Design of an airborne three-dimensional separation assistance display. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 41(5), 863–875 (2011)CrossRefGoogle Scholar
  5. 5.
    Radišić, T., Novak, D., Juričić, B.: Reduction of air traffic complexity using trajectory-based operations and validation of novel complexity indicators. IEEE Trans. Intell. Transp. Syst. 18(11), 3038–3048 (2017)CrossRefGoogle Scholar
  6. 6.
    Besada, J., Soto, A., de Miguel, G., García, J., Voet, E.: ATC trajectory reconstruction for automated evaluation of sensor and trajectoryer performance. IEEE Aerospace Electron. Syst. Mag. 28(2), 4–17 (2013)CrossRefGoogle Scholar
  7. 7.
    Sotiriou, D., Kopsaftopoulos, F., Fassois, S.: An adaptive time-series probabilistic framework for 4-D trajectory conformance monitoring. IEEE Trans. Intell. Transp. Syst. 17(6), 1606–1616 (2016)CrossRefGoogle Scholar
  8. 8.
    Wang, X.: Research on key techniques of real-time monitoring for aircraft flight safety. Nanjing University of Aeronautics and Astronautics (2008)Google Scholar
  9. 9.
    Lu, H., Deng, X.: Real-time flight trajectory security monitoring technology based-on ACARS. Aircraft Des. 6, 52–56 (2009)Google Scholar
  10. 10.
    Dai, S.: Study of adaptive cubic spline interpolation approximation algorithm. Dalian University of Technology (2008)Google Scholar
  11. 11.
    Li, X.: An adaptive algorithm for knots of cubic B-spline in data fitting. Dalian University of Technology (2008)Google Scholar
  12. 12.
    Chaimatanan, S., Delahaye, D., Mongeau, M.: A hybrid metaheuristic optimization algorithm for strategic planning of 4D aircraft trajectories at the continental scale. IEEE Comput. Intell. Mag. 9(4), 46–61 (2014)CrossRefGoogle Scholar
  13. 13.
    Wang, X., Shirinzadeh, B.: Nonlinear multiple integrator and application to aircraft navigation. IEEE Trans. Aerospace Electron. Syst. 50(1), 607–622 (2014)CrossRefGoogle Scholar
  14. 14.
    Margellos, K., Lygeros, J.: Toward 4-D trajectory management in air traffic control: a study based on monte carlo simulation and reachability analysis. IEEE Trans. Control Syst. Technol. 21(5), 1820–1833 (2013)CrossRefGoogle Scholar
  15. 15.
    Tang, J.: Review: analysis and improvement of traffic alert and collision avoidance system. IEEE Access 5, 21419–21429 (2017)CrossRefGoogle Scholar
  16. 16.
    Pritchett, A.R., Genton, A.: Negotiated decentralized aircraft conflict resolution. IEEE Trans. Intell. Transp. Syst. 19(1), 81–91 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lan Ma
    • 2
  • Shan Tian
    • 1
  • Yang Song
    • 1
  • Zhijun Wu
    • 1
    Email author
  • Meng Yue
    • 1
  1. 1.School of Electronic Information and AutomationCivil Aviation University of ChinaTianjinChina
  2. 2.School of Air Traffic ManagementCivil Aviation University of ChinaTianjinChina

Personalised recommendations