Advanced Defensive Tactics
Integrating Simulation and Machine Learning in Aerial Warfare
DOI:
https://doi.org/10.55972/spectrum.v25i1.403Keywords:
Threat reaction, Passive and semi-active missiles, Computer simulation, OptimizationAbstract
The study developed and evaluated the winding maneuver, a new defensive maneuver against threats, threat reaction, aimed at improving aircraft penetration into hostile territories against passive and semi-active surface-to-air missiles. The maneuver, based on navigation principles and evasive maneuvers, was simulated using the Aerospace Simulation Environment (ASA) software in combat scenarios. Data analysis involved experimental design, statistical testing, application of machine learning models, and optimization through metaheuristics. The results indicated that the winding maneuver significantly improves aircraft survival rates and mission success, as demonstrated by the Victory Capability Determinant (VCD) metric developed in the study. The optimized configuration of the maneuver, obtained using the Genetic Algorithm, was verified through new simulations, confirming its operational effectiveness.
References
R. E. Ball, The fundamentals of aircraft combat survivability analysis and design. American Institute of Aeronautics and Astronautics, 2003.
R. P. Birchenall, M. A. Richardson, B. Brian, and W. Roy, “Modelling an infrared Man Portable Air Defence System,” Infrared Phys Technol, vol. 53, no. 5, pp. 372–380, Sep. 2010, doi: 10.1016/j.infrared.2010.07.001.
L. H. Macedo, R. A. Scarpel, and H. C. Marques, “Utilização da heurística Extremal Optimization para alocação coordenada de múltiplos alvos em Combate Aéreo BVR,” Spectrum: Journal of Operational Applications in Defense Areas, vol. 23, no. 1, Aug. 2022, doi: 10.55972/SPECTRUM.V23I1.384.
V. L. Harshitha, K. G. S. Narayan, and J. A. Baskaradas, “A Study on Passive and Active Detection of Missiles,” 2022 URSI Regional Conference on Radio Science, USRI-RCRS 2022, 2022, doi: 10.23919/URSI-RCRS56822.2022.10118548.
R. L. Shaw, Fighter Combat: tactics and maneuvering. Naval Institute Press, 1985.
C. A. de M. Silvestre and L. de A. Faria, “Desenvolvimento de modelos de simulação para engajamento de mísseis infravermelhos,” Spectrum: Journal of Operational Applications in Defense Area, vol. 21, pp. 53–60, Jul. 2020, doi: 10.55972/SPECTRUM.V21I1.77.
D. A. James, Radar Homing Guidance for Tactical Missiles. Macmillan Education UK, 1986. doi: 10.1007/978-1-349-08602-3.
G. Feng, “The Basic Radio Homing Systems for Missiles (Part 2: Semi-active Homing System),” Guidance and Fuze, 2006.
F. Imado and S. Miwa, “Fighter evasive maneuvers against proportional navigation missile,” J Aircr, vol. 23, no. 11, pp. 825–830, 1986, doi: 10.2514/3.45388.
P. A. P. Suseno and R. A. Sasongko, “Development of Air Combat Effectiveness Simulation and Analysis Scheme for Beyond Visual Range (BVR) Case,” Applied Mechanics and Materials, vol. 842, pp. 329–336, Jun. 2016, doi: 10.4028/WWW.SCIENTIFIC.NET/AMM.842.329.
J. P. A. Dantas, D. Geraldo, A. N. Costa, M. R. O. A. Maximo, and T. Yoneyama, “ASA-SimaaS: Advancing Digital Transformation through Simulation Services in the Brazilian Air Force,” Sep. 2023, Accessed: May 17, 2024. [Online]. Available: https://arxiv.org/abs/2309.08680v1
J. P. A. Dantas, A. N. Costa, V. C. F. Gomes, A. R. Kuroswiski, F. L. L. Medeiros, and D. Geraldo, “ASA: A Simulation Environment for Evaluating Military Operational Scenarios,” Jun. 2022, [Online]. Available: http://arxiv.org/abs/2207.12084
P. Jiang, Q. Zhou, and X. Shao, Surrogate Model-Based Engineering Design and Optimization, 1st ed. in Springer Tracts in Mechanical Engineering. Singapore: Springer Singapore, 2020. doi: 10.1007/978-981-15-0731-1.
A. Gosavi, Simulation-Based Optimization, 2nd ed., vol. 55. in Operations Research/Computer Science Interfaces Series, vol. 55. Boston, MA: Springer US, 2015. doi: 10.1007/978-1-4899-7491-4.
M. F. Viscardi, A. Leandro De Castro, E. Luiz, F. Senne, and A. Passaro, “Better fit e busca tabu: Uma otimização para o apoio ao combate,” 25° Symposium on Operational Applications in Defense Areas - SIGE, 2023.
Z. Tian, M. Danino, Y. Bar-Shalom, and B. Milgrom, “Missile Threat Detection and Evasion Maneuvers With Countermeasures for a Low-Altitude Aircraft,” IEEE Trans Aerosp Electron Syst, vol. 59, no. 6, pp. 7352–7362, Dec. 2023, doi: 10.1109/TAES.2023.3287153.
BRASIL, “Norma do Comando de Preparo - NOPREP/OPR/14 - Treinamento de Navegação à Baixa Altura,” 2018.
A. R. Kuroswiski, F. L. L. Medeiros, M. M. De Marchi, and A. Passaro, “Beyond visual range air combat simulations: validation methods and analysis using agent-based models,” The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, Nov. 2023, doi: 10.1177/15485129231211915.
K. Yao and J. Gao, “Law of Large Numbers for Uncertain Random Variables,” IEEE Transactions on Fuzzy Systems, vol. 24, no. 3, pp. 615–621, Jun. 2016, doi: 10.1109/TFUZZ.2015.2466080.
A. M. Law and W. David. Kelton, Simulation modeling and analysis. McGraw-Hill, 2000.
D. C. Montgomery, Design and analysis of experiments, 8th ed. John Wiley & Sons, Inc., 2012.
T. J. Santner, B. J. Williams, and W. I. Notz, The Design and Analysis of Computer Experiments. in Springer Series in Statistics. New York, NY: Springer New York, 2018. doi: 10.1007/978-1-4939-8847-1.
Y. Guo, Y. Gao, H. Liu, and W. Gao, “Mission Simulation and Stealth Effectiveness Evaluation Based on Fighter Engagement Manager (FEM),” DEStech Transactions on Computer Science and Engineering, no. cece, Oct. 2017, doi: 10.12783/DTCSE/CECE2017/14488.
J. L. Deutsch and C. V. Deutsch, “Latin hypercube sampling with multidimensional uniformity,” J Stat Plan Inference, vol. 142, no. 3, pp. 763–772, Mar. 2012, doi: 10.1016/J.JSPI.2011.09.016.
A. N. Costa, “Sequential Optimization of Formation Flight Control Method Based on Artificial Potential Fields,” Instituto Tecnológico de Aeronáutica, São José dos Campos, 2019.
J. L. Devore, Probabilidade e Estatística para Engenharia e Ciências, 9o Edição. Cengage, 2019. Accessed: Jun. 03, 2024. [Online]. Available: https://www.ofitexto.com.br/probabilidade-e-estatistica-para-engenharia-e-ciencias/p
L. M. DeBruine and D. J. Barr, “Understanding Mixed-Effects Models Through Data Simulation,” https://doi.org/10.1177/2515245920965119, vol. 4, no. 1, Mar. 2021, doi: 10.1177/2515245920965119.
D. Mohakul, C. R. S. Kumar, S. Singh, S. Katti, and S. Chougule, “Health Monitoring of Ship’s Engine with Simulated Data by using Classifiers -Preliminary Result,” International Conference on Electrical Engineering and Information Communication Technology, Apr. 2023, doi: 10.1109/ICEEICT56924.2023.10157555.
M. Dolores. Ugarte, A. F. Militino, and A. T. Arnholt, Probability and Statistics with R, 2nd ed., vol. 1. CRC Press, Taylor & Francis Group, 2016.
G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning, 2nd ed., vol. 1. in Springer Texts in Statistics, vol. 1. New York, NY: Springer US, 2021. doi: 10.1007/978-1-0716-1418-1.
W. J. Wang, A. Bansal, C. S. Bennette, and A. Basu, “Mimicking Clinical Trials Using Real-World Data: A Novel Method and Applications,” Medical decision making, vol. 43, no. 3, pp. 275–287, Apr. 2022, doi: 10.1177/0272989X221141381.
J. Banks, J. S. C. II, B. L. Nelson, and D. M. Nicol, Discrete-Event System Simulation, 5th ed., vol. 1. Pearson Education, 2013.
W. L. Winston, Operations Research: Applications and Algorithms, 4th ed. Toronto, 2004.
T. Airforce, “F-16 Fighting Falcon Multirole Fighter,” Airforce Technology. Accessed: Jul. 01, 2023. [Online]. Available: https://www.airforce-technology.com/projects/f-16-fighting-falcon-multirole-fighter/
S. Y. Ong, B. L. Pierson, and C. F. Lin, “Optimal evasive aircraft maneuvers against a surface-to-air missile,” 1st IEEE Regional Conference on Aerospace Control Systems, AEROCS 1993 - Proceedings, pp. 475–482, 1993, doi: 10.1109/AEROCS.1993.720980.
R. Skomorokhov, “SAM ‘Thor’: the god of clear skies,” TOP WAR. Accessed: Jun. 26, 2024. [Online]. Available: https://en.topwar.ru/194715-zrk-tor-bog-chistogo-neba.html
L. Ai-Zhen, C. Li-yun, W. Yin-long, and W. Lu, “A Quantity Optimization Method on Integrated-Loading-and-Unloading-Missile Vehicles,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 11, no. 2, pp. 623–629, Feb. 2013, doi: 10.11591/TELKOMNIKA.V11I2.1985.
J. Huh, J. Park, D. Shin, and Y. Choi, “A Behavior Optimization Method for Unmanned Combat Aerial Vehicles Using Matrix Factorization,” IEEE Access, vol. 8, pp. 100298–100307, 2020, doi: 10.1109/ACCESS.2020.2998189.
A. Plaia, S. Buscemi, J. Fürnkranz, and E. L. Mencía, “Comparing Boosting and Bagging for Decision Trees of Rankings,” J Classif, vol. 39, no. 1, pp. 78–99, Mar. 2022, doi: 10.1007/S00357-021-09397-2/FIGURES/8.
C. M. Bishop, Pattern Recognition and Machine Learning, 2nd ed., vol. 1. New York, NY: Springer, 2006.
A. Géron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems, 2nd ed., vol. 1. CA 95472: O’Reilly Media, 2019. Accessed: Aug. 12, 2024. [Online]. Available: https://www.bibguru.com/b/how-to-cite-hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow-concepts-tools-and-techniques-to-build-intelligent-systems/
Y. A. Ali, E. M. Awwad, M. Al-Razgan, and A. Maarouf, “Hyperparameter Search for Machine Learning Algorithms for Optimizing the Computational Complexity,” Processes, vol. 11, no. 2, Feb. 2023, doi: 10.3390/PR11020349.
M. Gendreau and J.-Y. Potvin, Handbook of Metaheuristics, 3rd ed. Springer Cham, 2019. Accessed: Aug. 12, 2024. [Online]. Available: http://www.springer.com/series/6161
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