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A Study on Modeling of Fighter Pilots Using a dPCA-HMM

dPCA-HMM을 이용한 전투기 조종사 모델링 연구

  • Choi, Yerim (Department of Industrial Engineering, Seoul National University) ;
  • Jeon, Sungwook (Department of Industrial Engineering, Seoul National University) ;
  • Park, Jonghun (Department of Industrial Engineering, Seoul National University) ;
  • Shin, Dongmin (Department of Industrial and Management Engineering, Hanyang University)
  • Received : 2014.04.20
  • Accepted : 2014.12.23
  • Published : 2015.01.01

Abstract

Modeling of fighter pilots, which is a fundamental technology for war games using defense M&S (Modeling & Simulation) becomes one of the prominent research issues as the importance of defense M&S increases. Especially, the recent accumulation of combat logs makes it possible to adopt statistical learning methods to pilot modeling, and an HMM (Hidden Markov Model) which is able to utilize the sequential characteristic of combat logs is suitable for the modeling. However, since an HMM works only by using one type of features, discrete or continuous, to apply an HMM to heterogeneous features, type integration is required. Therefore, we propose a dPCA-HMM method, where dPCA (Discrete Principal Component Analysis) is combined with an HMM for the type integration. From experiments conducted on combat logs acquired from a simulator furnished by agency for defense development, the performance of the proposed model is evaluated and was satisfactory.

전투기 조종사 모델링은 국방 M&S(Modeling & Simulation)를 활용한 전쟁 모의 및 전투 실험의 기초 기술로 국방 M&S의 중요성이 대두됨에 따라 연구의 필요성이 높아지고 있다. 특히, 최근 전투 로그의 축적으로 통계적 학습 기법을 활용한 모델링의 적용이 가능해졌으며 전투 로그의 시계열적 특성을 반영할 수 있는 HMM(Hidden Markov Model)이 적합하다. 하지만 HMM은 이산형 혹은 연속형 중 한 형태의 변수만을 통해 학습되므로 이형 변수로 구성된 전투 로그에 적용을 위해서는 형변환 과정이 필요하다. 따라서 본 논문에서는 형변환을 위한 dPCA(Discrete Principal Component Analysis)와 HMM을 접목한 dPCA-HMM 기반 조종사 모델링 방법을 제안한다. 국방과학연구소 관급 시뮬레이터로부터 생성된 전투 로그를 이용한 비교 실험을 통해 제안하는 방법론의 성능을 평가하였으며, 만족스러운 성능을 나타내었다.

Keywords

References

  1. Jeong, S., "Required technology for implementing defense M&S," Industrial engineering magazine, vol. 20, no. 4, 2014, pp. 35-41.
  2. Jang, D., Cho, S., Tahk, M., Koo, H., and Kim, J., "Fuzzy Logic Based Collision Avoidance for UAVs," Journal of the Korean Society for Aeronautical and Space Sciences, vol. 34, no. 7, 2006, pp. 55-62. https://doi.org/10.5139/JKSAS.2006.34.7.055
  3. Won, D., Shim, S., Kim, K., Tahk, M., Seong, K., and Kim, E., "Track-Before-Detect Algorithm for Multiple Target Detection," Journal of the Korean Society for Aeronautical and Space Sciences, vol. 39, no. 9, 2011, pp. 848-857. https://doi.org/10.5139/JKSAS.2011.39.9.848
  4. Rabiner, L., "A tutorial on hidden Markov models and selected applications in speech recognition," Proceedings of the IEEE, vol. 77, no. 2, 1989, pp. 257-286. https://doi.org/10.1109/5.18626
  5. Lai, C., Lu, S. L., and Zhao, Q., "Performance analysis of speech recognition software," Proceedings of the Workshop on Computer Architecture Evaluation using Commercial Workloads, 2002.
  6. Hayashi, M., "Hidden Markov Models to identify pilot instrument scanning and attention patterns," Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2003, vol. 3, pp. 2889-2896.
  7. Choi, Y., Kwon, N., Lee, S., Shin Y., Ryo, C., Park, J., Shin, D., "Hypo-vigilance Detection for UCAV Operators Based on a Hidden Markov Model," Computational and Mathematical Methods in Medicine, 2014.
  8. Mori, R., Suzuki, S., "Modeling of Pilot Landing Approach Control Using Stochastic Switched Linear Regression Model," Journal of Aircraft, vol. 47, no. 5, 2010, pp. 1554-1558. https://doi.org/10.2514/1.C000204
  9. Lowe, C. D., "Predicting pilot intent and aircraft trajectory in uncontrolled airspace," Massachusetts Institute of Technology, 2014.
  10. Andersson, M., Petterson, G., "Improving situation awareness using aerial-mission recognition and temporal information," Proceedings of the International Conference on Information Fusion, Stockhholm, Sweden, 2004.
  11. Trevo, K., "Human adaptive mechatronics methods for mobile working machines," Aalto-yliopsiton teknillinen korkeakoulu, 2010.
  12. Quinlan, J. R., "Learning decision tree classifiers," ACM Computing Surveys, vol. 28, no. 1, 1996, pp. 71-72.
  13. Baek, J., Kim, C., and Kim, S., "Multi-Interval Discretization of Continuous Valued Attributes for Constructing Incremental Decision Tree," Journal of the Korean Institute of Industrial Engineers, vol. 27, no. 4., 2001, pp. 394.
  14. Schenk, J., Schwarzler, S., Ruske, G., and Rigoll, G., "Novel VQ designs for discrete HMM on-line handwritten whiteboard note recognition," Lecture Notes in Computer Science, vol. 5096, 2008, pp. 234-243.
  15. Krzanowski, W. J., "Discrimination and classification using both binary and continuous variables," Journal of the American Statistical Association, vol. 70, no. 352, 1975, pp. 782-790. https://doi.org/10.1080/01621459.1975.10480303
  16. Filmer, D. and Pritchett, L. H., "Estimating wealth effects without expenditure Data-Or tears: An application to educational enrollments in states of india," Demography, vol. 38, no. 1, 2001, pp. 115-132. https://doi.org/10.1353/dem.2001.0003
  17. Jolliffe, I., Principal component analysis, John Wiley & Sons, Ltd, 2005.
  18. Kim, J., Goo, Y., and Lee, H., "Signal-based Fault Diagnosis Algorithm of Control Surfaces of Small Fixed-wing Aircraft," Journal of the Korean Society for Aeronautical and Space Sciences, vol. 40, no. 12, 2012, pp. 1040-1047. https://doi.org/10.5139/JKSAS.2012.40.12.1040
  19. Dempster, A. P., Laird, N. M., and Rubin, D. B., "Maximum likelihood from incomplete data via the EM algorithm," Journal of the Royal statistical Society, vol. 39, no. 1, 1977, pp. 1-38.
  20. Kim, E., Helal, S., Cook, D., "Human Activity Recognition and Pattern," IEEE Transactions on Pervasive Computing, vol. 9, no. 1, 2010, pp. 48-53.
  21. Antwarg, L., Rokach, L., Shapira, B., "Attribute-Driven Hidden Markov Model Trees for Intention Prediction," IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 42, no. 6, 2012, pp. 1103-1119. https://doi.org/10.1109/TSMCC.2012.2198212
  22. Kiseleva, J., Lam, H. T., Pechenizkiy, M., Calders, T., "Predicting Current User Intent with Contextual Markov Models," Proceedings of the IEEE International Conference on Data Mining Workshops, 2013, pp. 391-398.