Journal of the Korean Society for Aeronautical & Space Sciences (한국항공우주학회지)

The Korean Society for Aeronautical and Space Sciences (한국항공우주학회)
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Approximate Dynamic Programming Based Interceptor Fire Control and Effectiveness Analysis for M-To-M Engagement

근사적 동적계획을 활용한 요격통제 및 동시교전 효과분석

  • Received : 2022.02.22
  • Accepted : 2022.03.28
  • Published : 2022.04.01
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Abstract

As low altitude long-range artillery threat has been strengthened, the development of anti-artillery interception system to protect assets against its attacks will be kicked off. We view the defense of long-range artillery attacks as a typical dynamic weapon target assignment (DWTA) problem. DWTA is a sequential decision process in which decision making under future uncertain attacks affects the subsequent decision processes and its results. These are typical characteristics of Markov decision process (MDP) model. We formulate the problem as a MDP model to examine the assignment policy for the defender. The proximity of the capital of South Korea to North Korea border limits the computation time for its solution to a few second. Within the allowed time interval, it is impossible to compute the exact optimal solution. We apply approximate dynamic programming (ADP) approach to check if ADP approach solve the MDP model within processing time limit. We employ Shoot-Shoot-Look policy as a baseline strategy and compare it with ADP approach for three scenarios. Simulation results show that ADP approach provide better solution than the baseline strategy.

저고도 궤적의 장사정포 위협이 대두됨에 따라 이를 방어할 요격 시스템의 개발이 시작될 예정이다. 이러한 장사정포의 공격을 방어하는 문제는 전형적인 동적 무기 표적 할당 문제다. 동적 무기 표적 할당 문제에서는 한 시점에서의 의사결정 결과가 이후 시점의 의사결정 과정에 영향을 주며, 이는 마코브 의사결정 모형의 특징이기도 하다. 장사정포의 공격을 방어하기 위한 의사결정 과정에 허용되는 시간은 공격자와 방어자의 거리를 고려할 때 저고도 궤적의 동시 다발성 발사체에 대한 대응은 수 초 이내에 결정되어야 하나, 짧은 시간 내에 마코브 의사결정 과정으로 최적해를 구하는 것은 불가능하다. 본 논문에서는 장사정포 공격을 방어하는 동적 무기 표적 할당 문제를 마코브 의사결정 문제로 나타내고, 3가지 시나리오를 작성한 후 근사적 동적계획 방법을 적용하여 요격이 가능 시간 안에 해의 도출이 가능한지를 시뮬레이션을 통하여 확인하였다. 도출된 해의 품질을 검증하기 위하여 각 시나리오에 대하여 근사적 동적계획을 적용한 결과와 Shoot-Shoot-Look 방법을 적용한 결과를 비교하였다. 시뮬레이션 결과, 장사정포의 방어 시나리오에 대하여 근사적 동적계획의 결과가 Shoot-Shoot-Look 방법을 이용한 결과보다 우수함을 보였다.

Keywords

References

  1. Choi, Y. H., Lee, Y. H. and Kim, J. E., "Comparative Study on Performance of Meta-heuristics for Weapon-Target Assignment Problem," Journal of the Korea Institute of Military Science and Technology, Vol. 20, No. 3, 2017, pp. 441~453. https://doi.org/10.9766/KIMST.2017.20.3.441
  2. Chosun media, https://www.chosun.com/politics/politics_general/2022/02/21/6SGFMAEIZZGG7PIZAMLVZ33HCU/. (accessed 2022/02/21)
  3. Defense White Paper, Ministry of National Defense, 2014, pp. 1~332.
  4. DEMA, https://kookbang.dema.mil.kr/newsWeb/m/20210720/1/BBSMSTR_000000100115/view.do?nav=0&nav2=0. (accessed 2022/02/21)
  5. Park, S. G. and Lee, K. H., "A Study on the Establishment of Capability-Based Multi-Layered Missile Defense System Considering MD in U.S.," Journal of the KNST, Vol. 3, No. 1, 2020, pp. 46~55. https://doi.org/10.31818/jknst.2020.03.3.1.46
  6. Yonhapnews, https://m.yna.co.kr/view/AKR20210628082800504?input=1195m. (accessed 2022/02/21)
  7. Hong, D. W., Yim, D. S. and Choi, B. W., "Application and Determination of Defended Footprint Using a Simulation Model for Ballistic Missile Trajectory," Journal of the Korea Institute of Military Science and Technology, Vol. 21, No. 4, 2018, pp. 551~561. https://doi.org/10.9766/KIMST.2018.21.4.551
  8. Jang, J. G., Kim, K., Choi, B. W. and Suh, J. J., "A Linear Approximation Model for an Asset-based Weapon Target Assignment Problem," Journal Society of Korea Industrial and System Engineering, Vol. 38, No. 3, 2015, pp. 108~116.
  9. Kim, J. H., Kim, K., Choi, B. W. and Suh, J.J., "An Application of Quantum-inspired Genetic Algorithm for Weapon Target Assignment Problem," Journal Society of Korea Industrial and System Engineering, Vol. 40, No. 4, 2017, pp. 260~267. https://doi.org/10.11627/jkise.2017.40.4.260
  10. Lloyd, S. P. and Witsenhausen, H. S, "Weapon Allocation is NP-Complete," Proceedings of the IEEE Summer Computer Simulation Conference, 1986, pp. 1054~1058.
  11. Ha, M. R. and Choi, J. W., "Simulator Design for Static Weapon Allocation Algorithm Evaluation," Journal of Institute of Control, Robotics and Systems, Vol. 25, No. 4, 2019, pp. 340~345. https://doi.org/10.5302/j.icros.2019.19.0005
  12. Yoon, M. H., Park, J. H., Yi, J. H. and Koo, B. J., "An Effective Weapon Assignment Algorithm in a Multi-Target and Multi-Weapon Environment," Korea Information Science Society, Winter Conference, 2016, pp. 87~89.
  13. Choi, B. W., Yoo, B. C., Kim, J. H. and Yim, D. S., "A Study on the Flight Trajectory Prediction Method of Ballistic Missiles," 2020 Autumn Conference on Journal of Korean Society of Systems Engineering, 2020, pp. 131~140.
  14. Yoo, B. C., Kim, J. H., Kwon, Y. S. and Choi, B. W., "A Study on the Flight Trajectory Prediction Method of Ballistic Missiles," Journal of Korean Society of Systems Engineering, Vol. 16, No. 2, 2020, pp. 131~140. https://doi.org/10.14248/JKOSSE.2020.16.2.131
  15. Im, J. S., Yoo, B. C., Kim, J. H. and Choi, B. W., "A Study of Multi-to-Majority Response on Threat Assessment and Weapon Assignment Algorithm: by Adjusting Ballistic Missiles and Long-Range Artillery Threat," Journal of Korean Society of Industrial and systems Engineering, Vol. 44 N0. 4, 2021, pp. 43~52.
  16. Yook, J. K., Hwang, S. J. and Kim, T. G., "Impact of MOPs on Effectiveness for M-to-M Engagement with the Counter Long Range Artillery Intercept System", Journal of Korean Society of Simulation, Vol. 29, No. 3, September 2020, pp. 57~72.
  17. Shin, M. K., Park, S.-S., Lee, D. and Choi, H.-L., "Mean Field Game based Reinforcement Learning for Weapon-Target Assignment", Journal of the Korea Institute of Military Science and Technology, Vol. 23, No. 4, 2020, pp. 337~345. https://doi.org/10.9766/KIMST.2020.23.4.337
  18. Davis, M. T., Robbins, M. J. and Lunday, B. J., "Approximate Dynamic Programming for Missile Defense Interceptor Fire Control," European Journal of Operational Research, 259, 2017, pp. 873~886. https://doi.org/10.1016/j.ejor.2016.11.023
  19. Summers, D. S., Robbins, M. J. and Lunday, B. J., "An Approximate Dynamic Programming for Comparing Firing Policies in a Networked Air Defense Environment," Computers & Operations Research 117, 2020 : 104890. https://doi.org/10.1016/j.cor.2020.104890
  20. Powell, W. B., Approximate Dynamic Programming: Solving the Curse of Dimensionality, 2011, Second Edition, John Wiley & Sons, Hoboken, NJ.
  21. Powell, W. B., "Perspectives of Approximate Dynamic Programming," Annals of Operations Research, Vol 13, No. 2, 2012, pp. 1~38.
  22. McKenna, R. S., Robbins, M. J., Lunday, B. J. and McCormack, I. M., "Approximate Dynamic Programming for the Military Inventory Routing Problem," Annals of Operationd Research, Vol. 288, No. 1, 2020, pp. 391~416. https://doi.org/10.1007/s10479-019-03469-8
  23. Bradtke, S. J. and Barto, A. G, "Linear Least-Squares Algorithms for Temporal Difference Learning," Machine Learning, Vol. 22, No. 1, 1996, pp. 33~57. https://doi.org/10.1007/BF00114723