Skip to main content
SpringerLink
Log in

Application of Machine Learning in UAV Combat

  • Conference paper
  • First Online:
Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) (ICAUS 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 861))

Included in the following conference series:

  • 99 Accesses

Abstract

With the rapid development of artificial intelligence technology, the form of warfare is undergoing profound changes, intelligent, unmanned, networked warfare has gradually become the mainstream form of warfare. As the core of artificial intelligence, machine learning has achieved great success in the fields of computer vision, natural language processing, and recommendation systems. For current UAV combat missions, machine learning also has great application potential. In this study, based on the traction of UAV combat requirements, we focus on the research and analysis of the application of machine learning in UAV situation awareness and autonomous decision making. With its powerful data analysis advantages, machine learning can assist UAV in extracting key information from massive battlefield information, and quickly realize the conversion from data information to decision making to improve the speed of battlefield reaction, planning and decision making. Therefore, the application of machine learning to situation awareness fusion and decision-making is of great significance and worthy of in-depth research and exploration.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 549.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 699.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 699.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Li, S., Wang, Y., Wu, C., et al.: Artificial Intelligence and unmanned warfare. In: 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 336–339. IEEE (2018)

    Google Scholar 

  2. Feng, S., Xi, J., Gong, C., et al.: A collaborative decision making approach for multi-unmanned combat vehicles based on the behaviour tree. In: 2020 3rd International Conference on Unmanned Systems (ICUS), pp. 395–400. IEEE (2020)

    Google Scholar 

  3. Hu, H., Fang, L., Yang, C., et al.: Research on development and countermeasures of army ground unmanned combat system. In: 2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT), pp. 654–657. IEEE (2020)

    Google Scholar 

  4. Duan, T., Wang, W., Li, X., et al.: Intelligent collaborative architecture design based on unmanned combat swarm. In: 2020 6th International Conference on Big Data and Information Analytics (BigDIA), pp. 463–470. IEEE (2020)

    Google Scholar 

  5. Yuksek, B., Umut, D.M., Inalhan, G., et al.: Cooperative planning for an unmanned combat aerial vehicle fleet using reinforcement learning. J. Aerosp. Inf. Syst. 18, 1–12 (2021)

    Google Scholar 

  6. Prakash, S.: Review of recent path planning optimisation algorithms for unmanned combat aerial vehicles. AIAA AVIATION 2020 FORUM, p. 2849 (2020)

    Google Scholar 

  7. Udeanu, G., Dobrescu, A., Oltean, M.: Unmanned aerial vehicle in military operations. In: The 18th International Conference Scientific Research and Education in the Air Force–AFASES, Brasov, Romania, pp. 199–205 (2016)

    Google Scholar 

  8. Liu, Z., Chen, M., Wu, Q., et al.: Prediction of unmanned aerial vehicle target intention under incomplete information. Sci. SINICA Inf. 50(5), 704–717 (2020)

    Article  Google Scholar 

  9. Wang, H., Cheng, H., Hao, H.: The use of unmanned aerial vehicle in military operations. In: Long, S., Dhillon, B.S. (eds.) Man-Machine-Environment System Engineering: Proceedings of the 20th International Conference on MMESE, pp. 939–945. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-6978-4_108

    Chapter  Google Scholar 

  10. Sun, L., Zhang, J., Chang, J., et al.: The evaluation of effectiveness for the collaborative combat of an unmanned aerial vehicle based on grey minimum entropy. J. Aerosp. Technol. Manage. 13 (2021)

    Google Scholar 

  11. Xiong, P., Liu, H., Tian, Y.: Mission effectiveness evaluation of manned/Unmanned aerial team based on OODA and agent-based simulation. In: Proceedings of the 2019 3rd International Conference on Artificial Intelligence and Virtual Reality, pp. 31–37 (2019)

    Google Scholar 

  12. Blasch, E., Pham, T., Chong, C.Y., et al.: Machine learning/Artificial intelligence for sensor data fusion-opportunities and challenges. IEEE Aerosp. Electron. Syst. Mag. 36(7), 80–93 (2021)

    Article  Google Scholar 

  13. Zohdi, T.I.: The game of drones: rapid agent-based machine-learning models for multi-UAV path planning. Comput. Mech. 65(1), 217–228 (2020)

    Article  MathSciNet  Google Scholar 

  14. Moir, C., Dean, J.: A machine learning approach to generic entity resolution in support of cyber situation awareness. In: ACSC, pp. 47–58 (2015)

    Google Scholar 

  15. Park, C.Y.: Multi-entity bayesian networks learning for predictive situation awareness. George Mason University (2017)

    Google Scholar 

  16. Jordan, M.I., Mitchell, T.M.: Machine learning: Trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)

    Article  MathSciNet  Google Scholar 

  17. Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning. MIT Press, Cambridge (2018)

    Google Scholar 

  18. Shinde, P.P., Shah, S.: A review of machine learning and deep learning applications. In: 2018 Fourth international conference on computing communication control and automation (ICCUBEA), pp. 1–6. IEEE (2018)

    Google Scholar 

  19. Wyatt, E.: The DARPA/air force unmanned combat air vehicle (UCAV) program. In: AIAA International Air and Space Symposium and Exposition: The Next 100 Years, p. 2616 (2013)

    Google Scholar 

  20. Stulberg, A.N.: Managing the unmanned revolution in the US Air Force. Orbis 51(2), 251–265 (2007)

    Article  Google Scholar 

  21. Force U S A. The US Air Force Remotely Piloted Aircraft and Unmanned Aerial Vehicle Strategic Vision (2005)

    Google Scholar 

  22. Feng, Z., Xiaofeng, H., Lin, W., et al.: From situation cognition stepped into situation intelligent cognition. J. Syst. Simul. 30(3), 761 (2018)

    Google Scholar 

  23. Dong, J., Wu, G., Yang, T., et al.: Battlefield situation awareness and networking based on agent distributed computing. Phys. Commun. 33, 178–186 (2019)

    Article  Google Scholar 

  24. Anicho, O., Charlesworth, P.B., Baicher, G.S., et al.: Situation awareness and routing challenges in unmanned HAPS/UAV based communications networks. In: 2020 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1175–1182. IEEE (2020)

    Google Scholar 

  25. Tulum, K., Durak, U., Yder, S.K.: Situation aware UAV mission route planning. In: 2009 IEEE Aerospace conference, pp. 1–12. IEEE (2009)

    Google Scholar 

  26. Alsalam, B.H.Y., Morton, K., Campbell, D., et al.: Autonomous UAV with vision based on-board decision making for remote sensing and precision agriculture. In: 2017 IEEE Aerospace Conference, pp. 1–12. IEEE (2017)

    Google Scholar 

  27. Lacroix, S., Alami, R., Lemaire, T., Hattenberger, G., Gancet, J.: Decision making in multi-UAVs systems: architecture and algorithms. In: Ollero, A., Maza, I. (eds.) Multiple heterogeneous unmanned aerial vehicles, pp. 15–48. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73958-6_2

    Chapter  Google Scholar 

  28. Haibin, D., Daifeng, Z., Yanming, F., Yimin, D.: From wolf pack intelligence to UAV swarm cooperative decision-making. Sci. Sin. Inf. 49(1), 112–118 (2019)

    Article  Google Scholar 

  29. Thammachantuek, I., Kosolsomnbat, S., Ketcham, M.: Comparison of machine learning algorithm's performance based on decision making in autonomous car. In: 2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), pp. 1–6. IEEE (2018)

    Google Scholar 

  30. Arulkumaran, K., Deisenroth, M.P., Brundage, M., et al.: Deep reinforcement learning: a brief survey. IEEE Sig. Process. Mag. 34(6), 26–38 (2017)

    Article  Google Scholar 

  31. Henderson, P., Islam, R., Bachman, P., et al.: Deep reinforcement learning that matters. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)

    Google Scholar 

  32. Fang, M., Li, Y., Cohn, T.: Learning how to active learn: a deep reinforcement learning approach. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 595–605 (2017)

    Google Scholar 

  33. Yosinski, J., Clune, J., Bengio, Y., et al.: How transferable are features in deep neural networks? Adv. Neural. Inf. Process. Syst. 27, 3320–3328 (2014)

    Google Scholar 

  34. Rath, S.P., Povey, D., Veselý, K., et al.: Improved feature processing for deep neural networks. In: Interspeech, pp. 109–113 (2013)

    Google Scholar 

  35. Tang, R., Zhuo, Z., Zhang, C., et al.: The applications of artificial intelligence in situation assessment and game countermeasure during unmanned air combat. In: 2019 IEEE International Conference on Unmanned Systems (ICUS), pp. 909–913. IEEE (2019)

    Google Scholar 

  36. Chen, Y., Zhang, J., Yang, Q., et al.: Design and verification of UAV maneuver decision simulation system based on deep q-learning network. In: 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 817–823. IEEE (2020)

    Google Scholar 

  37. Zijian, H.U., Xiaoguang, G.A.O., Kaifang, W.A.N., et al.: Relevant experience learning: a deep reinforcement learning method for UAV autonomous motion planning in complex unknown environments. Chin. J. Aeronaut. 34 (2021)

    Google Scholar 

  38. Yue, L.I., Xiaohui, Q.I.U., Xiaodong, L.I.U., et al.: Deep reinforcement learning and its application in autonomous fitting optimization for attack areas of UCAVs. J. Syst. Eng. Electron. 31(4), 734–742 (2020)

    Article  Google Scholar 

  39. Najgebauer, A., Antkiewicz, R., Tarapata, Z., et al.: The automation of combat decision processes in the simulation based operational training support system. In: 2007 IEEE Symposium on Computational Intelligence in Security and Defense Applications, pp. 145–152. IEEE (2007)

    Google Scholar 

  40. Sun, Y., Yuan, B., Zhang, T., et al.: Research and implementation of intelligent decision based on a priori knowledge and DQN algorithms in wargame environment. Electronics 9(10), 1668 (2020)

    Article  Google Scholar 

  41. Wang, L., Wang, W., Wang, Y., et al.: Feasibility of reinforcement learning for UAV-based target searching in a simulated communication denied environment. Sci. Sin. Inf. 50(3), 375–395 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Unit 92493, PLA, Huludao, China

    Yan Ren

  2. College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China

    Xiao-Qun Cao, Ke-Cheng Peng, Chao-Hao Xiao & Wen-Long Tian

  3. Naval Aeronautical University, Huludao, China

    Ya-Nan Guo

Authors
  1. Yan Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao-Qun Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ya-Nan Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ke-Cheng Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chao-Hao Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wen-Long Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China

    Meiping Wu

  2. College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China

    Yifeng Niu

  3. Beijing HIWING Scientific and Technological Information Institute, Beijing, China

    Mancang Gu

  4. Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory, Beijing, China

    Jin Cheng

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ren, Y., Cao, XQ., Guo, YN., Peng, KC., Xiao, CH., Tian, WL. (2022). Application of Machine Learning in UAV Combat. In: Wu, M., Niu, Y., Gu, M., Cheng, J. (eds) Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021). ICAUS 2021. Lecture Notes in Electrical Engineering, vol 861. Springer, Singapore. https://doi.org/10.1007/978-981-16-9492-9_290

Download citation

Publish with us

Policies and ethics

Search

Navigation