Skip to main content
SpringerLink
Log in

Aerodynamically Controlled Missile Flight Datasets and Its Applications

  • Original Paper
  • Published:
International Journal of Aeronautical and Space Sciences Aims and scope Submit manuscript

Abstract

This paper provides open flight datasets generated by extensive Monte Carlo simulations for an aerodynamically controlled missile under a fixed engagement scenario with the purpose of encouraging data analytics research in the missile application. With the fast advance in the field of data analytics, fueled by the recently developed machine learning and deep learning algorithms, the potential of applying the data analysis techniques to the domain of aerospace engineering is increasingly high, especially in the light of detecting anomalies and uncovering the hidden information in the flight data. A dataset is an essential ingredient in the research of data analytics and the development of new data analysis frameworks. However, since it is almost impossible or highly inefficient to construct a large dataset of guided missiles via physical experiments, a disclosed database is nearly non-existence. Even if this is possible, there is no open source dataset for academic research due to the security issue. Thus, by developing a high-fidelity 6-DOF (degree-of-freedom) simulation program, we generate realistic flight datasets of a guided missile, which can be used publicly. Furthermore, we provide illustrative examples of using the generated datasets for the purpose of demonstrating the potential of the application, that is, to detect abnormal data patterns to determine signs of the control loop instability during the flight.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Dani MC, Freixo C, Jollois FX, Nadif M ( 2015) Unsupervised anomaly detection for aircraft condition monitoring system. In: IEEE Aerospace Conference Proceedings June 1–7 2015 . https://doi.org/10.1109/AERO.2015.7119138

  2. Li L, Hansman RJ, Palacios R, Welsch R (2016) Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring. Transp Res Part C Emerg Technol 64:45–57. https://doi.org/10.1016/j.trc.2016.01.007

    Article  Google Scholar 

  3. Puranik TG, Mavris DN (2018) Anomaly detection in general-aviation operations using energy metrics and flight-data records. J Aerosp Inform Syst 15(1):22–35. https://doi.org/10.2514/1.I010582

    Article  Google Scholar 

  4. Lee KH, Lim SM, Cho DH, Kim HD (2020) Development of fault detection and identification algorithm using deep learning for nanosatellite attitude control system. Int J Aeronaut Space Sci 21(2):576–585. https://doi.org/10.1007/s42405-019-00235-9

    Article  Google Scholar 

  5. Freeman P, Pandita R, Srivastava N, Balas GJ (2013) Model-based and data-driven fault detection performance for a small UAV. IEEE/ASME Trans Mechatron 18(4):1300–1309. https://doi.org/10.1109/TMECH.2013.2258678

    Article  Google Scholar 

  6. Zhong Y, Zhang Y, Zhang W, Zuo J, Zhan H (2018) Robust actuator fault detection and diagnosis for a quadrotor UAV with external disturbances. IEEE Access 6:48169–48180. https://doi.org/10.1109/ACCESS.2018.2867574

    Article  Google Scholar 

  7. Hundman K, Constantinou V, Laporte C, Colwell I, Soderstrom T ( 2018) Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp 387– 395. ACM, New York. https://doi.org/10.1145/3219819.3219845

  8. Jin X, Cai S, Li H, Karniadakis GE ( 2021) NSFnets (Navier–Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations. J Comput Phys 426:109951. https://doi.org/10.1016/j.jcp.2020.109951

  9. Hou W, Darakananda D, Eldredge JD (2019) Machine-learning-based detection of aerodynamic disturbances using surface pressure measurements. AIAA J 57(12):5079–5093. https://doi.org/10.2514/1.J058486

    Article  Google Scholar 

  10. Lee J, Shin H, Kim T (2018) Optimal combination of fault detection and isolation methods of integrated navigation algorithm for UAV. Int J Aeronaut Space Sci 19(3):694–710. https://doi.org/10.1007/s42405-018-0057-8

    Article  Google Scholar 

  11. Srivastava AN ( 2005) Discovering system health anomalies using data mining techniques. In: Proceedings of the 2005 Joint Army Navy NASA Air Force Conference on Propulsion, vol. 2. Carleston, SC, pp 1– 11. https://c3.nasa.gov/dashlink/static/media/publication/Data_Mining_for_ISHM.pdf

  12. Antonini A, Guerra W, Murali V, Sayre-McCord T, Karaman S(2020) The Blackbird Dataset: A Large-Scale Dataset for UAV Perception in Aggressive Flight. Springer Proceedings in Advanced Robotics 11, 130– 139 ( 2020) arXiv:1810.01987. https://doi.org/10.1007/978-3-030-33950-0_12

  13. Zarchan P (2012) Tactical and strategic missile guidance, 6th edition. Atalanta, Georgia (2012). https://doi.org/10.2514/4.868948

  14. Lee C-H, Jun B-E, Lee J-I, Tahk M-J (2013) Nonlinear missile autopilot design via three loop topology and time-delay adaptation scheme. In: 2013 13th international conference on control, automation and systems (ICCAS 2013), pp 50–54. https://doi.org/10.1109/ICCAS.2013.6703862

  15. Lee C-H, Jun B-E, Lee J-I (2016) Connections between linear and nonlinear missile autopilots via three-loop topology. J Guid Control Dyn 39(6):1424–1430. https://doi.org/10.2514/1.G001565

    Article  MathSciNet  Google Scholar 

  16. Lee C-H, He S, Hong J-H (2020) Investigation on physical meaning of three-loop autopilot. Int J Control Autom Syst 18(11):2709–2720

    Article  Google Scholar 

  17. Zipfel PH (2014) Modeling and simulation of aerospace vehicle dynamics, 3rd edn. American Institute of Aeronautics and Astronautics, Virginia. https://doi.org/10.2514/4.102509

  18. Blake WB (1998) Missile Datcom: User’s Manual—1997 FORTRAN 90 Revision. U.S. Air Force Research Lab./Air Vehicles Directorate, Wright-Patterson AFB, Virginia

  19. Jeon IS, Lee JI (2010) Optimality of proportional navigation based on nonlinear formulation. IEEE Trans Aerosp Electron Syst 46(4):2051–2055. https://doi.org/10.1109/TAES.2010.5595614

    Article  Google Scholar 

  20. Chi H-S, Lee Y-I, Lee C-H, Choi H-L (2021) A practical optimal guidance scheme under impact angle and terminal acceleration constraints. Int J Aeronaut Space Sci 22(4):923–935

    Article  Google Scholar 

  21. Song K-R, Kim T-H, Lee C-H, Tahk M-J (2021) A new guidance algorithm against high-speed maneuvering target. Int J Aeronaut Space Sci 22(5):1170–1182

    Article  Google Scholar 

  22. Kim Y-C, Lee C-H, Kim T-H, Tahk M-J (2021) A new cooperative homing guidance of anti-ship missiles for survivability enhancement. Int J Aeronaut Space Sci 22(3):676–686

    Article  Google Scholar 

  23. Lee D, Tahk M-J, Lee C-H (2021) Optimal threshold of intermittent maneuver for target observability improvement. Int J Aeronaut Space Sci 22(4):911–922

    Article  Google Scholar 

  24. Prasad NR, Almanza-Garcia S, Lu TT (2009) Anomaly detection. Comput Mater Continua 14(1):1–22. https://doi.org/10.1145/1541880.1541882

  25. Daszykowski M, Walczak B (2009) Density-based clustering methods. Comprehens Chemometr 2:635–654. https://doi.org/10.1016/B978-044452701-1.00067-3

    Article  Google Scholar 

  26. Jung K-W, Lee C-H ( 2021) Anomaly detection for Monte–Carlo simulation data of missile control system. In: The proceedings of the 2021 Asia-Pacific international symposium on aerospace technology. Springer, Jeju

  27. Park J-C, Jung K-W, Kim Y-W, Lee C-H (2022) Anomaly detection method for missile flight data by attention-CNN architecture. J Inst Control Robot Syst 28(5):520–527. https://doi.org/10.5302/J.ICROS.2022.21.0237

    Article  Google Scholar 

  28. Devlin J, Chang MW, Lee K, Toutanova K ( 2019) BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL HLT 2019–2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies—Proceedings of the Conference 1(Mlm), 4171–4186. arXiv:1810.04805

Download references

Acknowledgements

This research is supported by Agency for Defense Development and Defense Acquisition Program Administration under the Intelligence Flight Control Research Project (Contract Number: UD200045CD).

Author information

Authors and Affiliations

  1. Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro, 291, Daejeon, 34141, Republic of Korea

    Ki-Wook Jung, Young-Won Kim & Chang-Hun Lee

Authors
  1. Ki-Wook Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Young-Won Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chang-Hun Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chang-Hun Lee.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jung, KW., Kim, YW. & Lee, CH. Aerodynamically Controlled Missile Flight Datasets and Its Applications. Int. J. Aeronaut. Space Sci. 24, 248–260 (2023). https://doi.org/10.1007/s42405-022-00531-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42405-022-00531-x

Keywords

Search

Navigation