Possible applications of artificial intelligence algorithms in F-16 aircraft

Krawczyk, Tomasz; Papis, Mateusz; Bielawski, Radosław; Rządkowski, Witold

doi:10.20858/sjsutst.2024.123.5

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Artykuł - szczegóły

Czasopismo

Zeszyty Naukowe. Transport / Politechnika Śląska

2024 | z. 123 | 101--131

Tytuł artykułu

Possible applications of artificial intelligence algorithms in F-16 aircraft

Autorzy

Krawczyk, Tomasz , Papis, Mateusz , Bielawski, Radosław , Rządkowski, Witold

Treść / Zawartość

Pełne teksty:

101_SJSUTST123_2024_Krawczyk_Papis_Bielawski_Rzadkowski.pdf

Pobierz

Warianty tytułu

Języki publikacji

Abstrakty

The F-16 aircraft, widely used by the Polish Army Air Force, requires modifications based on Artificial Intelligence (AI) algorithms to enhance its combat capabilities and performance. This study aims to develop comprehensive guidelines for this purpose by first describing F-16 systems and categorizing AI algorithms. Machine learning, deep learning, fuzzy logic, evolutionary algorithms, and swarm intelligence are reviewed for their potential applications in modern aircraft. Subsequently, specific algorithms applicable to F-16 systems are identified, with conclusions drawn on their suitability based on system features. The resultant analysis informs potential F-16 modifications and anticipates future AI applications in military aircraft, facilitating the guidance of new algorithmic developments and offering benefits to similar aircraft types. Moreover, directions for future research and development work are delineated.

Słowa kluczowe

samolot F-16 sztuczna inteligencja AI uczenie maszynowe głębokie uczenie algorytmy AI możliwości bojowe

F-16 aircraft artificial intelligence AI machine learning deep learning AI algorithms combat capabilities

Wydawca

Czasopismo

Zeszyty Naukowe. Transport / Politechnika Śląska

Rocznik

2024

Tom

z. 123

Strony

101--131

Opis fizyczny

Bibliogr. 148 poz.

Twórcy

autor

Krawczyk, Tomasz

Faculty of Economic Sciences, University of Warsaw, Długa 44/50 Street, 00-241 Warsaw, Poland, krawczyk@uw.edu.pl

autor

Papis, Mateusz

Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, Nowowiejska 24 Street, 00-665 Warsaw, Poland, mateusz.papis@pw.edu.pl

autor

Bielawski, Radosław

Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, Nowowiejska 24 Street, 00-665 Warsaw, Poland, radek.bielawski@gmail.com

autor

Rządkowski, Witold

Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, Nowowiejska 24 Street, 00-665 Warsaw, Poland, witold.rzadkowski@pw.edu.pl

Bibliografia

1. Adamski M. 2009. „Analiza możliwości bojowych samolotów F-16 i MiG-29”. Problemy Techniki Uzbrojenia 110(38): 41-46. [In English: „Analysis of combat capabilities of F-16 and MiG-29 aircraft”. Issues of Armament Technology].
2. Aksoy M., O. Ozdemir, G. Guner, et al. 2021. „Flight Trajectory Pattern Generalization and Abnormal Flight Detection with Generative Adversarial Network”. AIAA Scitech 2021 Forum. Reston, Virginia, American Institute of Aeronautics and Astronautics. DOI: https://doi.org/10.2514/6.2021-0775.
3. Ammalladene-Venkata M., O. Halbe, C. Seidel, et al. 2021. „Deep Learning based obstacle awareness from airborne optical sensors”. 77th Annual Vertical Flight Society Forum and Technology Display, FORUM 2021: The Future of Vertical Flight.
4. Andoga R., L. Főző, M. Schrötter, et al. 2019. „Intelligent Thermal Imaging-Based Diagnostics of Turbojet Engines”. Applied Sciences 9(11): 2253. DOI: https://doi.org/10.3390/app9112253.
5. Apostolidis A., M. Pelt, K. P. Stamoulis. 2020. „Aviation Data Analytics in MRO Operations: Prospects and Pitfalls”. 2020 Annual Reliability and Maintainability Symposium (RAMS), IEEE: 1-7. DOI: https://doi.org/10.1109/RAMS48030.2020.9153694.
6. Baumann S., U. Klingauf. 2020. „Modeling of aircraft fuel consumption using machine learning algorithms”. CEAS Aeronautical Journal 11(1): 277-287. DOI: https://doi.org/10.1007/s13272-019-00422-0.
7. Bleu Laine M.-H., T. G. Puranik, D. N. Mavris, B. Matthews. 2020. „Multiclass Multiple-Instance Learning for Predicting Precursors to Aviation Safety Events”. Journal of Aerospace Information Systems 19(1): 22-36. DOI: https://doi.org/10.2514/1.I010971.
8. Boskovic J., J. Redding. 2009. „Accommodation of Control Actuator Failures in Morphing Aircraft”. AIAA Guidance, Navigation, and Control Conference. Reston, Virigina, American Institute of Aeronautics and Astronautics. DOI: https://doi.org/10.2514/6.2009-5759.
9. Buraimah I. J. 2020 „Using modern clustering techniques for parametric fault diagnostics of turbofan engines”. Civil Aviation High Technologies 23(6): 20-27. DOI: https://doi.org/10.26467/2079-0619-2020-23-6-20-27.
10. Carassale L. 2012. „Analysis of aerodynamic pressure measurements by dynamic coherent structures”. Probabilistic Engineering Mechanics 28: 66-74. DOI: https://doi.org/10.1016/j.probengmech.2011.08.010.
11. Celikmih K., O. Inan, H. Uguz. 2020. „Failure Prediction of Aircraft Equipment Using Machine Learning with a Hybrid Data Preparation Method”. Scientific Programming 2020: 1-10. DOI: https://doi.org/10.1155/2020/8616039.
12. Chen S., S. Imai, W. Zhu, C. A.Varela. 2018. „Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics”. Handbook of Dynamic Data Driven Applications Systems. Cham, Springer International Publishing: 97-121. DOI: https://doi.org/10.1007/978-3-319-95504-9_5.
13. Chen T., H. Liu, Y. Wang. 2021. „Intention prediction of UAVs based on improved DDQN”. Journal of Physics: Conference Series 2010(1): 012129. DOI: https://doi.org/10.1088/1742-6596/2010/1/012129.
14. Chen Y. 2012. „Modeling of longitudinal unsteady aerodynamics at high angle-of-attack based on support vector machines”. 8th International Conference on Natural Computation, IEEE: 431-435. DOI: https://doi.org/10.1109/ICNC.2012.6234640.
15. Chen Y., J. Zhang, Q. Yang, et al. 2020. „Design and Verification of UAV Maneuver Decision Simulation System Based on Deep Q-learning Network”. 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE: 817-823. DOI: https://doi.org/10.1109/ICARCV50220.2020.9305467.
16. Cottrell M., P. Gaubert, C. Eloy, et al. 2009. „Fault Prediction in Aircraft Engines Using Self-Organizing Maps”. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): 37-44. DOI: https://doi.org/10.1007/978-3-642-02397-2_5.
17. Czech Piotr. 2013. „Diagnosing a car engine fuel injectors' damage”. Communications in Computer and Information Science 395: 243-250. DOI: https://doi.org/10.1007/978-3-642-41647-7_30. Springer, Berlin, Heidelberg. ISBN: 978-3-642-41646-0; 978-3-642-41647-7. ISSN: 1865-0929. In: Mikulski Jerzy (eds), Activities of transport telematics, 13th International Conference on Transport Systems Telematics, Katowice Ustron, Poland, October 23-26, 2013.
18. Czech Piotr. 2011. „Diagnosing of disturbances in the ignition system by vibroacoustic signals and radial basis function - preliminary research”. Communications in Computer and Information Science 239: 110-117. DOI: https://doi.org/10.1007/978-3-642-24660-9_13. Springer, Berlin, Heidelberg. ISBN: 978-3-642-24659-3. ISSN: 1865-0929. In: Mikulski Jerzy (eds), Modern transport telematics, 11th International Conference on Transport Systems Telematics, Katowice Ustron, Poland, October 19-22, 2011.
19. Czech Piotr. 2012. „Identification of Leakages in the Inlet System of an Internal Combustion Engine with the Use of Wigner-Ville Transform and RBF Neural Networks”. Communications in Computer and Information Science 329: 175-182. DOI: https://doi.org/10.1007/978-3-642-34050-5_47. Springer, Berlin, Heidelberg. ISBN: 978-3-642-34049-9; 978-3-642-34050-5. ISSN: 1865-0929. In: Mikulski Jerzy (eds), Telematics in the transport environment, 12th International Conference on Transport Systems Telematics, Katowice Ustron, Poland, October 10-13, 2012.
20. Czech Piotr. 2013. „Intelligent Approach to Valve Clearance Diagnostic in Cars”. Communications in Computer and Information Science 395: 384-391. DOI: https://doi.org/10.1007/978-3-642-41647-7_47. Springer, Berlin, Heidelberg. ISBN: 978-3-642-41646-0; 978-3-642-41647-7. ISSN: 1865-0929. In: Mikulski Jerzy (eds), Activities of transport telematics, 13th International Conference on Transport Systems Telematics, Katowice Ustron, Poland, October 23-26, 2013.
21. Dantas J. P. A., A. N. Costa, D. Geraldo, et al. 2021. „Engagement Decision Support for Beyond Visual Range Air Combat”. 2021 Latin American Robotics Symposium (LARS), 2021 Brazilian Symposium on Robotics (SBR), and 2021 Workshop on Robotics in Education (WRE), IEEE: 96-101. DOI: https://doi.org/10.1109/LARS/SBR/WRE54079.2021.9605380.
22. DARPA. 2020. „AlphaDogfight Trials Foreshadow Future of Human-Machine Symbiosis”. Available at: https://www.darpa.mil/news-events/2020-08-26.
23. DARPA. 2023. „ACE Program’s AI Agents Transition from Simulation to Live Flight”. Available at: https://www.darpa.mil/news-events/2023-02-13.
24. Davis S. 2014. General Dynamics F-16 Fighting Falcon Manual. Sparford, Hayness Publishing.
25. Dong Y., J. Tao, Y. Zhang, et al. 2021. „Deep Learning in Aircraft Design, Dynamics, and Control: Review and Prospects”. IEEE Transactions on Aerospace and Electronic Systems 57(4): 2346-2368. DOI: https://doi.org/10.1109/TAES.2021.3056086.
26. Drouot A., H. Noura, L. Goerig, P. Piot. 2015. „Actuators additive SVD-based fault-tolerant control for a combat aircraft”. 2015 IEEE Conference on Control Applications (CCA), IEEE: 960-965. DOI: https://doi.org/10.1109/CCA.2015.7320736.
27. ElSaid A., F. El Jamiy, J. Higgins, et al. 2018. „Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration”. Applied Soft Computing 73: 969-991. DOI: https://doi.org/10.1016/j.asoc.2018.09.013.
28. Faure C., M. Olteanu, J.-M. Bardet, J. Lacaille. 2017. „Using self-organizing maps for clustering anc labelling aircraft engine data phases”. 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM), IEEE: 1-8. DOI: https://doi.org/10.1109/WSOM.2017.8020013.
29. Fawkes A.J., L.C.M. Menzel. 2018. „The Future Role of Artificial Intelligence Military Opportunities and Challenges”. JAPCC 27: 70-77.
30. Gao X., W. Huo, X. Feng. 2014. „Civil aircraft’s exceedance event diagnosis method based on fuzzy associative classifier”. Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics 10: 1366-1371. DOI: https://doi.org/10.13700/j.bh.1001-5965.2013.0656.
31. Gao Z., C. Ma, D. Song, Y. Liu. 2017. „Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis”. Neurocomputing 238: 13-23. DOI: https://doi.org/10.1016/j.neucom.2017.01.032.
32. Gao Z., A. Song. 2019. „Aviation Surveillance Information Fusion Technology Based on Recurrent Neural Network”. Proceedings the 9th International Workshop on Computer Science and Engineering, WCSE. DOI: https://doi.org/10.18178/wcse.2019.06.050.
33. Garcia C.E., M.R. Camana, I. Koo. 2021. „Machine learning-based scheme for multi-class fault detection in turbine engine disks”. ICT Express 7(1): 15-22. DOI: https://doi.org/10.1016/j.icte.2021.01.009.
34. Géron A. 2019. Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: concepts, tools, and techniques to build intelligent systems. O’Reilly Media.
35. Ginoulhac R., F. Barbaresco, J.-Y. Schneider, et al. 2019. „Target Classification Based On Kinematic Data From AIS/ADS-B, Using Statistical Features Extraction and Boosting”. 20th International Radar Symposium (IRS), IEEE: 1-10. DOI: https://doi.org/10.23919/IRS.2019.8768094.
36. Greet L.D., S. Harris. 2011. „Detection and clutter suppression using fusion of conventional CFAR and two parameter CFAR”. 3rd International Conference on Electronics Computer Technology, IEEE: 216-219. DOI: https://doi.org/10.1109/ICECTECH.2011.5941740.
37. Guanglei M., Z. Runnan, W. Biao, et al. 2021. „Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat”. International Journal of Aerospace Engineering 2021: 1-18. DOI: https://doi.org/10.1155/2021/9558838.
38. Guo H., H. Xu, L. Liu. 2010 „Target threat assessment of air combat based on support vector machines for regression”. Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics 36(1): 123-126.
39. Guo W., Y. Xian, D. Zhang, et al. 2019. „Hybrid IRBM-BPNN approach for error parameter estimation of SINS on aircraft”. Sensors 19(17): 3682. DOI: https://doi.org/10.3390/s19173682.
40. Han P., Z. Zhai, L. Zhang. 2020. „A Model-Based Approach to Optimizing Partition Scheduling of Integrated Modular Avionics Systems”. Electronics 9(8): 1281. DOI: https://doi.org/10.3390/electronics9081281.
41. Hitchens T. 2020. „AI Slays Top F-16 Pilot In DARPA Dogfight Simulation”. Available at: https://breakingdefense.com/2020/08/ai-slays-top-f-16-pilot-in-darpa-dogfight-simulation/.
42. Hu D., R. Yang, J. Zuo, et al. 2021. „Application of Deep Reinforcement Learning in Maneuver Planning of Beyond-Visual-Range Air Combat”. IEEE Access 9: 32282-32297. DOI: https://doi.org/10.1109/ACCESS.2021.3060426.
43. Hu D., J. Zuo, W. Zheng, et al. 2021. „Research on Application of LSTM-QDN in Intelligent Air Combat Simulation”. Journal of Physics: Conference Series 1746(1): 012028. DOI: https://doi.org/10.1088/1742-6596/1746/1/012028.
44. Hu J., P. Bhowmick, I. Jang, et al. 2021. „A Decentralized Cluster Formation Containment Framework for Multirobot Systems”. IEEE Transactions on Robotics 37(6): 1936-1955. DOI: https://doi.org/10.1109/TRO.2021.3071615.
45. Hu J., A.E. Turgut, T. Krajnik, et al. 2022. „Occlusion-Based Coordination Protocol Design for Autonomous Robotic Shepherding Tasks”. IEEE Transactions on Cognitive and Developmental Systems 14(1): 126-135. DOI: https://doi.org/10.1109/TCDS.2020.3018549.
46. Hu J., A.E. Turgut, B. Lennox, F. Arvin 2022. „Robust Formation Coordination of Robot Swarms With Nonlinear Dynamics and Unknown Disturbances: Design and Experiments”. IEEE Transactions on Circuits and Systems II: Express Briefs 69(1): 114-118. DOI: https://doi.org/10.1109/TCSII.2021.3074705.
47. Hu X., P. Luo, X. Zhang, J. Wang. 2018. „Improved Ant Colony Optimization for Weapon-Target Assignment”. Mathematical Problems in Engineering 2018: 1-14. DOI: https://doi.org/10.1155/2018/6481635.
48. Huang C., K. Dong, H. Huang, S. Tang. 2018. „Autonomous air combat maneuver decision using Bayesian inference and moving horizon optimization. Journal of Systems Engineering and Electronics 29(1): 86-97. DOI: https://doi.org/10.21629/JSEE.2018.01.09.
49. Huang J., X. Li, Z. Yang, et al. 2021. „A Novel Elitism Co-Evolutionary Algorithm for Antagonistic Weapon-Target Assignment”. IEEE Access 9: 139668-139684. DOI: https://doi.org/10.1109/ACCESS.2021.3119363.
50. Hunt E. 2021. „AI and the future of air combat”. Available at: https://www.aerosociety.com/news/ai-and-the-future-of-air-combat/.
51. İşci H., G.Ö. Günel. 2022. „Fuzzy logic based air-to-air combat algorithm for unmanned air vehicles”. International Journal of Dynamics and Control 10(1): 230-242. DOI: https://doi.org/10.1007/s40435-021-00803-6.
52. Jia Z., Z. Liu, Y. Cai. 2021. „A novel fault diagnosis method for aircraft actuator based on ensemble model”. Measurement 176: 109235. DOI: https://doi.org/10.1016/j.measurement.2021.109235.
53. Jia Z.Z., X.G. Fan, M.H. Xue, S. Zhang 2018. „Online Identification Method for Tactical Maneuver of Target Based on Air Combat Maneuver Element”. Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology 38(8): 820-827. DOI: https://doi.org/10.15918/j.tbit1001-0645.2018.08.009.
54. Jiang H. 2019. „Deep Learning Theory with Application in Intelligent Fault Diagnosis of Aircraft”. Journal of Mechanical Engineering 55(7): 27. DOI: https://doi.org/10.3901/JME.2019.07.027.
55. Julian K.D., M.J. Kochenderfer. 2019. „Distributed Wildfire Surveillance with Autonomous Aircraft Using Deep Reinforcement Learning”. Journal of Guidance, Control, and Dynamics 42(8): 1768-1778. DOI: https://doi.org/10.2514/1.G004106.
56. Kaplan A., M. Haenlein. 2019. „Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence”. Business Horizons 62(1): 15-25. DOI: https://doi.org/10.1016/j.bushor.2018.08.004.
57. Kim J., C. Zhang, S.I. Briceno, D.N. Mavris. 2021. „Supervised Machine Learning-based Wind Prediction to Enable Real-Time Flight Path Planning”. AIAA Scitech 2021 Forum. Reston, Virginia, American Institute of Aeronautics and Astronautics. DOI: https://doi.org/10.2514/6.2021-0519.
58. Kniaz V.V., M.I. Kozyrev, A.N. Bordodymov, et al. 2019. „Segmentation and visualization of obstacles for the enhanced vision system using generative adversarial networks”. Scientific Visualization 11(4): 43-52. DOI: https://doi.org/10.26583/sv.11.4.04.
59. Kong W., D. Zhou, Z. Yang, et al. 2020. „Maneuver Strategy Generation of UCAV for within Visual Range Air Combat Based on Multi-Agent Reinforcement Learning and Target Position Prediction”. Applied Sciences 10(15): 5198. DOI: https://doi.org/10.3390/app10155198.
60. Kong W., D. Zhou, Z. Yang, et al. 2020. „UAV autonomous aerial combat maneuver strategy generation with observation error based on state-adversarial deep deterministic policy gradient and inverse reinforcement learning”. Electronics 9(7): 1121. DOI: https://doi.org/10.3390/electronics9071121.
61. Kong W., D. Zhou, Z. Yang. 2020. „Air Combat Strategies Generation of CGF Based on MADDPG and Reward Shaping”. Proceedings – 2020 International Conference on Computer Vision, Image and Deep Learning, CVIDL 2020, IEEE: 651-655. DOI: https://doi.org/10.1109/CVIDL51233.2020.000-7.
62. Kong W., D. Zhou, K. Zhang, Z. Yang. 2020. „Air combat autonomous maneuver decision for one-on-one within visual range engagement base on robust multi-agent reinforcement learning”. IEEE 16th International Conference on Control & Automation (ICCA), IEEE: 506-512. DOI: https://doi.org/10.1109/ICCA51439.2020.9264567.
63. Kraemer A.D., E. Villani, D.H. Arjoni. 2019. „Aircraft FDI and human factors analysis of a take-off maneuvre using SIVOR flight simulator”. IFAC-PapersOnLine. DOI: https://doi.org/10.1016/j.ifacol.2019.01.063.
64. Kumar A., A.K. Ghosh. 2019. „Decision Tree– and Random Forest-Based Novel Unsteady Aerodynamics Modeling Using Flight Data”. Journal of Aircraft 56(1): 403-409. DOI: https://doi.org/10.2514/1.C035034.
65. Kuvayskova Y.E. 2021. „Application of Ensemble Machine Leearnings Methods for Predicting the Technical State of an Object”. Izvestiya of Samara Scientific Center of the Russian Academy of Sciences 23(1): 111-114. DOI: https://doi.org/10.37313/1990-5378-2021-23-1-111-114.
66. Lee H., T.G. Puranik, D.N. Mavris. 2021. „Deep Spatio-Temporal Neural Networks for Risk Prediction and Decision Support in Aviation Operations”. Journal of Computing and Information Science in Engineering 21(4): 1-21. DOI: https://doi.org/10.1115/1.4049992.
67. Lei D., S.S. Zhong. 2013. „Aircraft engine health signal denoising based on singular value decomposition and empirical mode decomposition methods”. Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) 43(3): 764-770. DOI: https://doi.org/10.7964/jdxbgxb201303034.
68. Lewitowicz J. 2021. Podstawy eksploatacji statków powietrznych. Tom 1. Statek powietrzny i elementy teorii. Warsaw, Wydawnictwo Instytutu Technicznego Wojsk Lotniczych. [In English: Fundamentals of aircraft operations. Volume 1. Aircraft and elements of theory].
69. Li G., H. Lee, A. Rai, A. Chattopadhyay. 2020. „Analysis of operational and mechanical anomalies in scheduled commercial flights using a logarithmic multivariate Gaussian model”. Transportation Research Part C: Emerging Technologies 110: 20-39. DOI: https://doi.org/10.1016/j.trc.2019.11.011.
70. Li P. 2020. „Research on radar signal recognition based on automatic machine learning”. Neural Computing and Applications 32(7): 1959-1969. DOI: https://doi.org/10.1007/s00521-019-04494-1.
71. Li S., K. Yang., J. Ma, et al. 2021. „Anti-interference recognition method of aerial infrared targets based on the Bayesian network”. Journal of Optics 50(2): 264-277. DOI: https://doi.org/10.1007/s12596-021-00701-2.
72. Liang X., J. Huang, Y. Zhou. 2010. „Study on the effectiveness evaluation method of formation Beyond Visual Range air combat based on genetic BP Neural Network”. Proceedings of 2010 Asia-Pacific International Symposium on Aerospace Technology APISAT 2010.
73. Liu P., Y. Ma 2017. „A Deep Reinforcement Learning Based Intelligent Decision Method for UCAV Air Combat”. Communications in Computer and Information Science 751: 274-286. DOI: https://doi.org/10.1007/978-981-10-6463-0_24.
74. Liu X., L. Liu, L. Wang, et al. 2019. „Performance Sensing Data Prediction for an Aircraft Auxiliary Power Unit Using the Optimized Extreme Learning Machine”. Sensors 19(18): 3935. DOI: https://doi.org/10.3390/s19183935.
75. Luo P., J. Xie, W. Che. 2016. „Q-learning based air combat target assignment algorithm”. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE: 000779-000783. DOI: https://doi.org/10.1109/SMC.2016.7844336.
76. Lyu Y., A. Yang, Z. Li, Z. Xi. 2021. „Research on the construction method of air combat decision knowledge”. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 43(7): 1866-1874. DOI: https://doi.org/10.12305/j.issn.1001-506X.2021.07.18.
77. Ma J., H. Zhao, D. Yang, Q. Kang. 2019. „Design and Optimization of Deep Convolutional Neural Network for Aircraft Target Classification”. Laser & Optoelectronics Progress 56(23): 231006. DOI: https://doi.org/10.3788/LOP56.231006.
78. Ma W., H. Li, Z. Wang, et al. 2021. „Close air combat maneuver decision based on deep stochastic game”. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 9: 14. DOI: https://doi.org/10.12305/j.issn.1001-506X.2021.02.19.
79. Matusiak W. 1996. „F-16 C”. Przegląd Konstrukcji Lotniczych. [In English: „F-16 C”. Aircraft Structures Review].
80. McCarthy J., M.L. Minsky., C.E. Shannon. 1955. „A proposal for the Dartmouth summer research project on artificial intelligence”. AI Magazine 27(4).
81. Minglang C., D. Haiwen, W. Zhenglei, S. QingPeng. 2018. „Maneuvering decision in short range air combat for unmanned combat aerial vehicles”. 2018 Chinese Control And Decision Conference (CCDC), IEEE: 1783-1788. DOI: https://doi.org/10.1109/CCDC.2018.8407416.
82. Mulgund S., K. Harper, G. Zacharias, K. Krishnakumar. 2001. „Large-Scale Air Combat Tactics Optimization Using Genetic Algorithms”. Journal of Guidance, Control, and Dynamics 24(1): 140-142. DOI: https://doi.org/10.2514/2.4689.
83. Müller A., S. Guido. 2018. Introduction to Machine Learning with Python: A Guide for Data Scientists. O’Reilly Media.
84. Napolitano M.R., D.A. Windon, J.L. Casanova, et al. 1998. „Kalman filters and neural-network schemes for sensor validation in flight control systems”. IEEE Transactions on Control Systems Technology 6(5): 596-611. DOI: https://doi.org/10.1109/87.709495.
85. Ning S., J. Sun, C. Liu, Y. Yi. 2021. „Applications of deep learning in big data analytics for aircraft complex system anomaly detection”. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 235(5): 923-940. DOI: https://doi.org/10.1177/1748006X211001979.
86. Niu D., Y. Wu, Y. Xu, et al. 2021. „Modeling of anti-jamming effectiveness evaluation of infrared air-to-air missile”. Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics 47(9): 1874-1883. DOI: https://doi.org/10.13700/j.bh.1001-5965.2020.033.
87. Novák V., I. Perfilieva, J. Močkoř. 1999. Mathematical Principles of Fuzzy Logic. Boston, MA, Springer US. DOI: https://doi.org/10.1007/978-1-4615-5217-8.
88. Papis M., T. Krawczyk. 2022. „Comparison of MiG-29 and F-16 aircraft in the field of susceptibility to destruction in combat”. Aviation 26(3): 131-137. DOI: https://doi.org/10.3846/aviation.2022.17592.
89. Patel A .A. 2019. Hands-On Unsupervised Learning Using Python. O’Reilly Media.
90. Peng K., R. Jiao, J. Dong, Y. Pi. 2019. „A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter”. Neurocomputing 361: 19-28. DOI: https://doi.org/10.1016/j.neucom.2019.07.075.
91. Qian X., Y. Kai, B. Ya, et al. 2018. „Data Mining and Simulation Analysis of Aircraft Control System Based on Apriori Algorithm”. IEEE CSAA Guidance, Navigation and Control Conference (CGNCC), IEEE: 1-6. DOI: https://doi.org/10.1109/GNCC42960.2018.9019047.
92. Quirion N., D. Liu. 2021. „Effect of Sensor Sensitivity on Autonomous Aerial Vehicle Target Localization”. Unmanned Systems 09(01): 11-21. DOI: https://doi.org/10.1142/S2301385021500023.
93. Quirion N., D. Liu, A. Ludu, D. Vincenzi. 2015. „Sensor sensitivity and reinforcement learning models on target acquisition task for autonomous vehicle”. IIE Annual Conference and Expo 2015.
94. Recht B.A. 2019. „Tour of Reinforcement Learning: The View from Continuous Control”. Annual Review of Control, Robotics, and Autonomous Systems. DOI: https://doi.org/10.1146/annurev-control-053018-023825.
95. Remadna I., L.S. Terrissa, S. Ayad, N. Zerhouni. 2021. „RUL Estimation Enhancement using Hybrid Deep Learning Methods”. International Journal of Prognostics and Health Management 12(1). DOI: https://doi.org/10.36001/ijphm.2021.v12i1.2378.
96. Rose R.L., T.G. Puranik, D.N. Mavris. 2020. „Natural Language Processing Based Method for Clustering and Analysis of Aviation Safety Narratives”. Aerospace 7(10): 143. DOI: https://doi.org/10.3390/aerospace7100143.
97. Roudbari A., F. Saghafi. 2014. „Intelligent modeling and identification of aircraft nonlinear flight”. Chinese Journal of Aeronautics 27(4): 759-771. DOI: https://doi.org/10.1016/j.cja.2014.03.017.
98. Rountree J., P. Hipelius, B. Dienst, et al. 2021. „Testing Artificial Intelligence in High-Performance, Tactical Aircraft”. 2021 IEEE Aerospace Conference (50100), IEEE: 1-15. DOI: https://doi.org/10.1109/AERO50100.2021.9438308.
99. Sarker I.H. 2021. „Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions”. SN Computer Science 2(6): 420. DOI: https://doi.org/10.1007/s42979-021-00815-1.
100. Sasaki H., C.G. Willcocks, T.P. Breckon. 2021. „Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery”. 25th International Conference on Pattern Recognition (ICPR), IEEE: 5083-5090. DOI: https://doi.org/10.1109/ICPR48806.2021.9413023.
101. Savitha R., A. Ambikapathi, K. Rajaraman. 2020. „Online RBM: Growing Restricted Boltzmann Machine on the fly for unsupervised representation”. Applied Soft Computing 92: 106278. DOI: https://doi.org/10.1016/j.asoc.2020.106278.
102. Schimert J. 2008. „Data-driven fault detection based on process monitoring using dimension reduction techniques”. IEEE Aerospace Conference Proceedings. DOI: https://doi.org/10.1109/AERO.2008.4526621.
103. Scukins E, P. Ogren. 2021. „Using Reinforcement Learning to Create Control Barrier Functions for Explicit Risk Mitigation in Adversarial Environments”. IEEE International Conference on Robotics and Automation (ICRA), IEEE: 10734-10740. DOI: https://doi.org/10.1109/ICRA48506.2021.9561853.
104. Sheridan K., T.G. Puranik, E. Mangortey, et al. 2020. „An Application of DBSCAN Clustering for Flight Anomaly Detection During the Approach Phase”. AIAA Scitech 2020 Forum. Reston, Virginia, American Institute of Aeronautics and Astronautics. DOI: https://doi.org/10.2514/6.2020-1851.
105. Shi J., Y. Li, G. Wang, X. Li. 2016. „Health index synthetization and remaining useful life estimation for turbofan engines based on run-to-failure datasets”. Eksploatacja i Niezawodnosc - Maintenance and Reliability 18(4): 621-631. DOI: https://doi.org/10.17531/ein.2016.4.18.
106. Shi W., Y.H. Feng, G.Q. Cheng, et al. 2021. „Research on Multi-aircraft Cooperative Air Combat Method Based on Deep Reinforcement Learning”. Zidonghua Xuebao/Acta Automatica Sinica 47(7): 1610-1623. DOI: https://doi.org/10.16383/j.aas.c201059.
107. Shin D.-H., Y. Kim. 2004. „Reconfigurable Flight Control System Design Using Adaptive Neural Networks”. IEEE Transactions on Control Systems Technology 12(1): 87-100. DOI: https://doi.org/10.1109/TCST.2003.821957.
108. Socha V., L. Hanakova, L. Socha, et al. 2018. „Automatic Detection of Flight Maneuvers with the Use of Density-based Clustering Algorithm”. XIII International Scientific Conference – New Trends in Aviation Development (NTAD), IEEE: 132–136. DOI: https://doi.org/10.1109/NTAD.2018.8551680.
109. Song X., J. Jiang, H. Xu. 2017. „Application of improved simulated annealing genetic algorithm in cooperative air combat”. Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University 38(11): 1762-1768. DOI: https://doi.org/10.11990/jheu.201606094.
110. Su M.-C., S.-C. Lai, S.-C. Lin, L.-F. You. 2012. „A new approach to multi-aircraft air combat assignments”. Swarm and Evolutionary Computation 6: 39-46. DOI: https://doi.org/10.1016/j.swevo.2012.03.003.
111. Sun Z., H. Piao, Z. Yang, et al. 2021. „Multi-agent hierarchical policy gradient for Air Combat Tactics emergence via self-play”. Engineering Applications of Artificial Intelligence 98: 104112. DOI: https://doi.org/10.1016/j.engappai.2020.104112.
112. Sutton R., A. Barto. 2018. Reinforcment Learning: An Introduction. MIT Press Cambridge.
113. Szopa M. 2020. „F-16 nadal popularny”. Available at: https://defence24.pl/przemysl/f-16-nadal-popularny-analiza. [In English: „F-16 still popular”].
114. Tang Q., W. He. 2021. „Target Recognition Method of Laser Imaging Fuze Based on Deep Transfer Learning”. 3rd International Conference on Natural Language Processing (ICNLP), IEEE: 202-207. DOI: https://doi.org/10.1109/ICNLP52887.2021.00040.
115. Taşdelen O., L. Çarkacioglu, B.U. Töreyin. 2021. „Anomaly Detection on ADS-B Flight Data Using Machine Learning Techniques”. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): 771-783. DOI: https://doi.org/10.1007/978-3-030-88081-1_58.
116. Tomaszek H., M. Wróblewski 2001. Podstawy oceny efektywności eksploatacji systemów uzbrojenia lotniczego. Warsaw, Bellona. [In English: Basics of evaluating the effectiveness of the operation of aircraft weapon systems].
117. Trusova K.V. 2021. „Machine Learning Methods Application for the Avionics Systems Health Analysis and Faults Localization Challenges”. Springer Proceedings in Physics: 383-397. DOI: https://doi.org/10.1007/978-3-030-58868-7_44.
118. Ummah K., H. Setiadi, H.M. Pasaribu, D. Anandito. 2020. „A Simple Fight Decision Support System for BVR Air Combat Using Fuzzy Logic Algorithm”. AVIA 1(1). DOI: https://doi.org/10.47355/avia.v1i1.8.
119. Uzun M., M. Umut Demirezen, E. Koyuncu, G. Inalhan. 2019. „Design of a Hybrid Digital-Twin Flight Performance Model Through Machine Learning”. IEEE Aerospace Conference, IEEE: 1-14. DOI: https://doi.org/10.1109/AERO.2019.8741729.
120. Valiant Air Command. „F-16 Fighting Falcon”. Available at: https://www.valiantaircommand.com/f-16-fighting-falcon.
121. Vanhoy G., T. Schucker, T. Bose. 2017. „Classification of LPI radar signals using spectral correlation and support vector machines”. Analog Integrated Circuits and Signal Processing 91(2): 305-313. DOI: https://doi.org/10.1007/s10470-017-0944-0.
122. Verma H.O., N.K. Peyada. 2020. „Parameter estimation of aircraft using extreme learning machine and Gauss-Newton algorithm”. The Aeronautical Journal 124(1272): 271–295. DOI: https://doi.org/10.1017/aer.2019.123.
123. Vikhar P.A. 2016. „Evolutionary algorithms: A critical review and its future prospects”. 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), IEEE: 261-265. DOI: https://doi.org/10.1109/ICGTSPICC.2016.7955308.
124. Wang B. 2020. „Data cleaning method of ADS-B historical flight trajectories”. Jiaotong Yunshu Gongcheng Xuebao/Journal of Traffic and Transportation Engineering 20(4): 217-226. DOI: https://doi.org/10.19818/j.cnki.1671-1637.2020.04.018.
125. Wang C., X. Zhang, L. Cong, et al. 2019. „Research on intelligent collision avoidance decision-making of unmanned ship in unknown environments”. Evolving Systems 10(4): 649-658. DOI: https://doi.org/10.1007/s12530-018-9253-9.
126. Wang L., P. Lucic, K. Campbell, C. Wanke. 2021. „Autoencoding Features for Aviation Machine Learning Problems”. AIAA AVIATION 2021 FORUM. DOI: https://doi.org/10.2514/6.2021-2388.
127. Wang L., J. Hu, Z. Xu, C. Zhao. 2021. „Autonomous maneuver strategy of swarm air combat based on DDPG”. Autonomous Intelligent Systems 1(1): 15. DOI: https://doi.org/10.1007/s43684-021-00013-z.
128. Wang M., L. Wang, T. Yue, H. Liu. 2020. „Influence of unmanned combat aerial vehicle agility on short-range aerial combat effectiveness”. Aerospace Science and Technology 96: 105534. DOI: https://doi.org/10.1016/j.ast.2019.105534.
129. Wang Z., H. Li, H. Wu, Z. Wu. 2020. „Improving Maneuver Strategy in Air Combat by Alternate Freeze Games with a Deep Reinforcement Learning Algorithm”. Mathematical Problems in Engineering 2020: 1-17. DOI: https://doi.org/10.1155/2020/7180639.
130. Wasilewski A. 2004. „Samolot myśliwski F-16 C/D Block 52+”. Typy Broni i Uzbrojenia. [In English: „F-16 C/D Block 52+ fighter aircraft”. Types of Weapons and Armaments].
131. Wu X., Y. Wu, B. Chen, et al. 2021. „Hierarchical Prediction of Infrared interference Environment based on Mutual Information and Random Forest”. Journal of Physics: Conference Series 1883(1): 012091. DOI: https://doi.org/10.1088/1742-6596/1883/1/012091.
132. Xi Z., A. Xu, Y. Kou, et al. 2020. „Target threat assessment in air combat based on PCA-MPSO-ELM algorithm”. Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica 41(9): 323895-323895. DOI: https://doi.org/10.7527/S1000-6893.2020.23895.
133. Xi Z., A. Xu, Y. Kou, et al. 2021. „Air combat target threat assessment based on gray principal component”. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 43(1): 147-155. DOI: https://doi.org/10.3969/j.issn.1001-506X.2021.01.18.
134. Xiao L., J. Huang, Z. S. Xu. 2013. „Cooperative attack decision for BVR air combat based on neural network”. Shenyang Gongye Daxue Xuebao/Journal of Shenyang University of Technology 35(3): 338-344. DOI: https://doi.org/10.7688/j.issn.1000-1646.2013.03.18.
135. Xiao L., J. Huang, Z. Xu. 2013. „Modeling air combat situation assessment based on combat area division”. Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics 39(10): 1309-1313.
136. Xie J., Q. Yang, S. Dai, et al. 2020. „Air Combat Maneuver Decision Based on Reinforcement Genetic Algorithm”. Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38(6): 1330-1338. DOI: https://doi.org/10.1051/jnwpu/20203861330.
137. Xu A., Y.X. Kou, L. Yu, Z.W. Li. 2012. „Stealthy engagement maneuvering strategy with Q-learning based on RBFNN for air vehicles”. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 34(1): 97-101. DOI: https://doi.org/10.3969/j.issn.1001-506X.2012.01.18.
138. Xue Y., Z. Chen. 2019. „Aviation material classification research based on consumption volatility clustering analysis”. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 41(12): 2802-2806. DOI: https://doi.org/10.3969/j.issn.1001-506X.2019.12.19.
139. Yang Q., J. Zhang, G. Shi, et al. 2020. „Maneuver Decision of UAV in Short-Range Air Combat Based on Deep Reinforcement Learning”. IEEE Access 8. DOI: https://doi.org/10.1109/ACCESS.2019.2961426.
140. Yang Q., Y. Zhu, J. Zhang, et al. 2019. „UAV Air Combat Autonomous Maneuver Decision Based on DDPG Algorithm”. IEEE 15th International Conference on Control and Automation (ICCA), IEEE: 37-42. DOI: https://doi.org/10.1109/ICCA.2019.8899703.
141. Yang Y., X. Hu, M. Rong, et al. 2019. „Characteristic Index Digging of Combat SoS Capability Based on Machine Learning”. Xitong Fangzhen Xuebao / Journal of System Simulation 31(6): 1048-1054. DOI: https://doi.org/10.16182/j.issn1004731x.joss.19-0238.
142. Yildirim Dalkiran F., M. Toraman. 2021. „Predicting thrust of aircraft using artificial neural networks”. Aircraft Engineering and Aerospace Technology 93(1): 35-41. DOI: https://doi.org/10.1108/AEAT-05-2020-0089.
143. You S., M. Diao, L. Gao, et al. 2020. „Target tracking strategy using deep deterministic policy gradient”. Applied Soft Computing 95: 106490. DOI: https://doi.org/10.1016/j.asoc.2020.106490.
144. Yuan W., P. Jia, L. Wang, L. Shao. 2009. „Aircraft Pose Recognition Using Locally Linear Embedding”. 2009 International Conference on Measuring Technology and Mechatronics Automation, IEEE: 454-457. DOI: https://doi.org/10.1109/ICMTMA.2009.637.
145. Yuksek B., U.M. Demirezen, G. Inalhan. 2021. „Development of UCAV Fleet Autonomy by Reinforcement Learning in a Wargame Simulation Environment”. AIAA Scitech 2021 Forum. Reston, Virginia, American Institute of Aeronautics and Astronautics. DOI: https://doi.org/10.2514/6.2021-0175.
146. Zhao Y., T. Kendall, B. Johnson. 2016. „Big Data and Deep Analytics Applied to the Common Tactical Air Picture (CTAP) and Combat Identification (CID)”. Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, SCITEPRESS - Science and Technology Publications: 443-449. DOI: https://doi.org/10.5220/0006086904430449.
147. Zhou W., M. Kuang, J. Zhu. 2020. „An unmanned air combat system based on swarm intelligence”. SCIENTIA SINICA Informationis 50(3): 363-374. DOI: https://doi.org/10.1360/SSI-2019-0196.
148. Zou Y. M., B. Wang. 2008. „Estimation of the attitude of a rolling missile based on independent component analysis”. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 7: 1301-1303.

Typ dokumentu

Bibliografia

Identyfikatory

DOI

10.20858/sjsutst.2024.123.5

Identyfikator YADDA

bwmeta1.element.baztech-2a42b040-5602-4d39-aea9-515fe15b7d12