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Random Forest Method and Simulation-based Effect Analysis for Real-time Target Re-designation in Missile Flight

유도탄의 실시간 표적 재지정을 위한 랜덤 포레스트 기법과 시뮬레이션 기반 효과 분석

  • Received : 2018.02.12
  • Accepted : 2018.04.26
  • Published : 2018.06.30

Abstract

The study of air defense against North Korean tactical ballistic missiles (TBM) should consider the rapidly changing battlefield environment. The study for target re-designation for intercept missiles enables effective operation of friendly defensive assets as well as responses to dynamic battlefield. The researches that have been conducted so far do not represent real-time dynamic battlefield situation because the hit probability for the TBM, which plays an important role in the decision making process, is fixed. Therefore, this study proposes a target re-designation algorithm that makes decision based on hit probability which considers real-time field environment. The proposed method contains a trajectory prediction model that predicts the expected trajectory of the TBM from the current position and velocity information by using random forest and moving window. The predicted hit probability can be calculated through the trajectory prediction model and the simulator of the intercept missile, and the calculated hit probability becomes the decision criterion of the target re-designation algorithm for the missile. In the experiment, the validity of the methodology used in the TBM trajectory prediction model was verified and the superiority of using the hit probability through the proposed model in the target re-designation decision making process was validated.

북한의 전술탄도미사일(TBM, tactical ballistic missile)에 대한 방공 분야 연구는 빠른 속도로 변화하는 전장 환경을 고려해야 한다. 아군 유도탄의 표적 재지정 연구는 동적인 전장에 대한 대응뿐만 아니라 아군 방어 자산의 효과적인 운용을 가능하게 한다. 현재까지 진행된 연구는 의사 결정 과정에서 중요한 역할을 하는 TBM의 명중 확률이 고정된 값이기 때문에 실시간 전장 상황을 대변하지 못한다. 따라서 본 연구는 실시간 전장 환경을 고려한 명중 확률을 기반으로 의사 결정을 내리는 표적 재지정 알고리즘을 제안한다. 제안 방법론은 랜덤 포레스트와 무빙윈도우(moving window) 기법을 사용하여 현재 TBM의 위치 및 속도 정보로 TBM의 예상 궤적을 예측하는 궤적 예측 모형을 포함한다. 예상 명중 확률은 궤적 예측 모형과 유도탄의 시뮬레이터를 통해서 계산할 수 있으며, 계산된 명중 확률은 유도탄에 대한 표적 재지정 알고리즘의 의사결정 기준이 된다. 실험에서는 TBM 궤적 예측 모형에 사용한 방법론의 타당성이 검증되었으며, 표적 재지정 의사 결정 과정에서 제안된 모델을 통해 명중 확률을 사용하는 것의 우수성이 확인되었다.

Keywords

References

  1. Ahuja, R. K., Kumar, A., Jha, K. C., & Orlin, J. B. (2007) "Exact and heuristic algorithms for the weapon-target assignment problem", Operations Research, 55(6), 1136-1146. https://doi.org/10.1287/opre.1070.0440
  2. Bhowmik, S., & Roy, C. (2007) "Application of extended kalman filter to tactical ballistic missile re-entry problem", arXiv preprint arXiv:0707.1918.
  3. Bisht, S. (2004) "Hybrid genetic-simulated annealing algorithm for optimal weapon allocation in multilayer defence scenario", Defence Science Journal, 54 (3), 395. https://doi.org/10.14429/dsj.54.2054
  4. Blodgett, D. E., Gendreau, M., Guertin, F., Potvin, J. Y., & Seguin, R. (2003) "A tabu search heuristic for resource management in naval warfare", Journal of Heuristics, 9(2), 145-169. https://doi.org/10.1023/A:1022525529778
  5. Bogdanowicz, Z. R. (2009) "A new efficient algorithm for optimal assignment of smart weapons to targets", Computers & Mathematics with Applications, 58 (10), 1965-1969. https://doi.org/10.1016/j.camwa.2009.07.082
  6. Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984), "Classification and regression trees", CRC press.
  7. Eckler, A. R., & Burr, S. A. (1972) "Mathematical models of target coverage and missile allocation", MILITARY OPERATIONS RESEARCH SOCIETY ALEXANDRIA VA.
  8. Gutman, O., & Palmor, Z. J. (2011) "Proportional navigation against multiple targets", Journal of Guidance, Control, and Dynamics, 34(6), 1728-1733. https://doi.org/10.2514/1.53373
  9. Hoerl, A. E., & Kennard, R. W. (1970) "Ridge regression: Biased estimation for nonorthogonal problems", Technometrics, 12(1), 55-67. https://doi.org/10.1080/00401706.1970.10488634
  10. Kalman, R. E. (1960) "A new approach to linear filtering and prediction problems", Journal of basic Engineering, 82(1), 35-45. https://doi.org/10.1115/1.3662552
  11. Karasakal, O. (2008) "Air defense missile-target allocation models for a naval task group", Computers & Operations Research, 35(6), 1759-1770. https://doi.org/10.1016/j.cor.2006.09.011
  12. Kirmse, A. (2004) "Game Programming Gems 4 (Game Programming Gems Series)", Charles River Media, Inc..
  13. Kim, D. Y (2014) "Discussion: North Korean ballistic missile threat and inconsistency of THAAD effectiveness in the Korean peninsula", Unification Strategy Forum Report, 56(1), 53-54.
  14. Leboucher, C., Shin, H. S., Le Menec, S., Tsourdos, A., & Kotenkoff, A. (2013) "Optimal weapon target assignment based on an geometric approach", IFAC Proceedings Volumes, 46(19), 341-346.
  15. Leboucher, C., Shin, H. S., Le Menec, S., Tsourdos, A., Kotenkoff, A., Siarry, P., & Chelouah, R. (2014) "Novel evolutionary game based multi-objective optimisation for dynamic weapon target assignment", IFAC Proceedings Volumes, 47(3), 3936-3941.
  16. Lee, Z. J., Lee, C. Y., & Su, S. F. (2002a) "An immunity-based ant colony optimization algorithm for solving weapon-target assignment problem", Applied Soft Computing, 2(1), 39-47. https://doi.org/10.1016/S1568-4946(02)00027-3
  17. Lee, Z. J., Su, S. F., & Lee, C. Y. (2002b) "A genetic algorithm with domain knowledge for weapon‐target assignment problems", Journal of the Chinese Institute of Engineers, 25(3), 287-295. https://doi.org/10.1080/02533839.2002.9670703
  18. Lee, Z. J., Su, S. F., & Lee, C. Y. (2003) "Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics", IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 33(1), 113-121. https://doi.org/10.1109/TSMCB.2003.808174
  19. Liu, C., Shui, P., & Li, S. (2011) "Unscented extended Kalman filter for target tracking", Journal of Systems Engineering and Electronics, 22(2), 188- 192. https://doi.org/10.3969/j.issn.1004-4132.2011.02.002
  20. Ljung, L. (1977) "The extended Kalman filter as a parameter estimator for linear systems", Linkoping University.
  21. Mitchell, T. M. (1997) "Machine learning", WCB.
  22. Naeem, H., & Masood, A. (2010) "An optimal dynamic threat evaluation and weapon scheduling technique", Knowledge-Based Systems, 23(4), 337-342. https://doi.org/10.1016/j.knosys.2009.11.012
  23. Noh, S., & Gmytrasiewicz, P. J. (1998) "Coordination and belief update in a distributed anti-air environment. In System Sciences", Proceedings of the Thirty-First Hawaii International Conference on (Vol. 5, pp. 142-151). IEEE.
  24. Siouris, G. M., Chen, G., & Wang, J. (1997) "Tracking an incoming ballistic missile using an extended interval Kalman filter", IEEE Transactions on Aerospace and Electronic Systems, 33(1), 232-240. https://doi.org/10.1109/7.570753
  25. Tibshirani, R. (1996) "Regression shrinkage and selection via the lasso", Journal of the Royal Statistical Society. Series B (Methodological), 267-288.
  26. Wu, C., & Han, C. (2007) "Strong tracking finite- difference extended Kalman filtering for ballistic target tracking. In Robotics and Biomimetics", IEEE International Conference on (pp. 1540-1544). IEEE.
  27. Xin, B., Chen, J., Zhang, J., Dou, L., & Peng, Z. (2010) "Efficient decision makings for dynamic weapon-target assignment by virtual permutation and tabu search heuristics", IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 649-662. https://doi.org/10.1109/TSMCC.2010.2049261