Volume 31 Issue 1
Jan.  2022
Turn off MathJax
Article Contents
LI Bo, LIANG Shiyang, CHEN Daqing, et al., “A Decision-Making Method for Air Combat Maneuver Based on Hybrid Deep Learning Network,” Chinese Journal of Electronics, vol. 31, no. 1, pp. 107-115, 2022, doi: 10.1049/cje.2020.00.075
Citation: LI Bo, LIANG Shiyang, CHEN Daqing, et al., “A Decision-Making Method for Air Combat Maneuver Based on Hybrid Deep Learning Network,” Chinese Journal of Electronics, vol. 31, no. 1, pp. 107-115, 2022, doi: 10.1049/cje.2020.00.075

A Decision-Making Method for Air Combat Maneuver Based on Hybrid Deep Learning Network

doi: 10.1049/cje.2020.00.075
Funds:  This work was supported by the National Natural Science Foundation of China (61573285, 62003267), Open Fund of Key Laboratory of Data Link Technology of China Electronics Technology Group Corporation (CLDL20182101), and Natural Science Foundation of Shaanxi Province (2020JQ-220)
More Information
  • Author Bio:

    (corresponding author) received the B.S. degree in electronic information technology and the M.S., Ph.D. degrees in systems engineering from Northwestern Polytechnical University, Xi’an, China. He is currently an Associate Professor with the School of Electronics and Information, Northwestern Polytechnical University. His current research interests include intelligent command and control, deep reinforcement learning and uncertain information processing. (Email: libo803@nwpu.edu.cn)

    received the B.S. degree in detection guidance and control technology from Northwestern Polytechnical University, Xi’an, China. She is currently a postgraduate student with the School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China. (Email: 17802933853@mail.nwpu.edu.cn)

    received the B.S. degree from Xidian University, Xi’an, China. He is currently a Professor with School of Engineering, London South Bank University, London, UK. His current research interest is data mining. (Email: chend@lsbu.ac.uk)

    received the B.S. degree in detection guidance and control technology and the M.S. degree in systems engineering from Northwestern Polytechnical University, Xi’an, China. (Email: 545005922@qq.com)

  • Received Date: 2020-03-13
  • Accepted Date: 2021-01-06
  • Available Online: 2021-08-18
  • Publish Date: 2022-01-05
  • In this paper, a hybrid deep learning network-based model is proposed and implemented for maneuver decision-making in an air combat environment. The model consists of stacked sparse auto-encoder network for dimensionality reduction of high-dimensional, dynamic time series combat-related data and long short-term memory network for capturing the quantitative relationship between maneuver control variables and the time series combat-related data after dimensionality reduction. This model features: using time series data as the basis of decision-making, which is more in line with the actual decision-making process; using stacked sparse auto-encoder network to reduce the dimension of time series data to predict the result more accurately; in addition, taking the maneuver control variables as the output to control the maneuver, which makes the maneuver process more flexible. The relevant experiments have demonstrated that the proposed model can effectively improve the prediction accuracy and convergence rate in the prediction of maneuver control variables.
  • loading
  • [1]
    X. M. He, W. Z. Chang, H. X. Chang, et al., “Autonomous maneuvering decision research of UAV based on experience knowledge representation,” 2016 Chinese Control & Decision Conference, Yinchuan, pp.161–166, 2016.
    [2]
    W. Zu, Y. Gao, H.X. Chang, et al., “A UAV formation maneuvering decision algorithm based on heuristic tactics knowledge,” 2017 29th Chinese Control & Decision Conference, Chongqing, pp.7280–7284, 2017.
    [3]
    S. Ku, D. L. Ding, C. Q. Huang, et al., “Optimal tactical maneuver control model based on improved genetic algorithm,” Journal of Fire and Command Control, vol.43, no.12, pp.21–26, 2014.
    [4]
    P. Qian, D. Y. Zhou, J. C. Huang, et al., “Maneuver decision for cooperative close-range air combat based on state predicted influence diagram,” IEEE International Conference on Information & Automation, Macau, pp.726–731, 2017.
    [5]
    C. Q. Huang, K. X. Zhao, B. J. Han, et al., “A method for UAV maneuver decision based on approximate dynamic programming,” Journal of Electronics and Information, vol.40, no.10, pp.2447–2452, 2008.
    [6]
    J. Fang, L. M. Zhang, W. Fang, et al., “Approximate dynamic programming for CGF air combat maneuvering decision,” IEEE International Conference on Computer and Communications, Chengdu, pp.1386–1390, 2016.
    [7]
    B. K. Fan, Y. LI, R. Y. Zhang, et al., “Review on the technological development and application of UAV systems,” Chinese Journal of Electronics, vol.29, no.2, pp.199–207, 2020. doi: 10.1049/cje.2019.12.006
    [8]
    C. Q. Huang, K. S. Dong, H. Q. Huang, et al., “Autonomous air combat maneuver decision using Bayesian inference and moving horizon optimization,” Journal of Systems Engineering and Electronics, vol.29, no.1, pp.86–97, 2018. doi: 10.21629/JSEE.2018.01.09
    [9]
    G. L. Meng, Y. Q. Luo, X. Liang, et al., “An air combat decision method based on dynamic bayesian networks,” Journal of Command Control and Simulation, vol.39, no.3, pp.49–54, 2017.
    [10]
    X. Chen, X. G. Zhang, W. J. Wang, et al., “Multi-objective Monte-Carlo tree search based aerial maneuvering control,” IEEE Chinese Guidance, Navigation and Control Conference, Nanjing, pp.81–87, 2016.
    [11]
    Y. Y. Gao, M. J. Yu, Q. S. Han, et al., “Air combat maneuver decision-making based on improved symbiotic organisms search algorithm,” Journal of Beijing University of Aeronautics and Astronautics, vol.45, no.03, pp.429–436, 2019.
    [12]
    X. He, X. N. Jing, and C. Feng, “Air combat maneuver decision based on MCTS method,” Journal of Airforce Engineering University (Natural Science Edition), vol.18, no.5, pp.36−41, 2017
    [13]
    K. F. Wan, X. G. Gao, Z. J. Hu, et al., “Robust motion control for UAV in dynamic uncertain environments using deep reinforcement learning,” Remote Sensing, vol.12, no.4, article no.640, 2020. doi: 10.3390/rs12040640
    [14]
    X. B. Zhang, G. Q. Liu, C. J. Yang, et al., “Research on air confrontation maneuver decision-making method based on reinforcement learning,” Journal of Electronics, vol.7, no.11, article no.279, 2018. doi: 10.3390/electronics7110279
    [15]
    B. Li, Z. P. Yang, D. Q. Chen, et al., “Maneuvering target tracking of UAV based on MN-DDPG and transfer learning,” Defence Technology, vol.17, no.2, pp.457–466, 2020.
    [16]
    Y. S. Zhang, W. Zu, Y. Gao, et al., “Research on autonomous maneuvering decision of UCAV based on deep reinforcement learning,” 2018 30th Chinese Control & Decision Conference, Shenyang, pp.230–235, 2018.
    [17]
    B. Li, Z. G. Gan, D. Q. Chen, et al., “UAV maneuvering target tracking in uncertain environments based on deep reinforcement learning and meta-learning,” Remote Sensing, vol.12, no.22, article no.3789, 2020.
    [18]
    H. Xue, Q. D. Huynh, and M. Reynolds, “SS-LSTM: A hierarchical LSTM model for pedestrian trajectory prediction,” IEEE Winter Conference on Applications of Computer Vision, Lake Tahoe, NV, pp.1186–1194, 2018.
    [19]
    T. S. Wu, S. Y. Chen, Y. M. Tian, et al., “A feature optimized deep learning model for clinical data mining,” Chinese Journal of Electronics, vol.29, no.03, pp.476–481, 2020. doi: 10.1049/cje.2020.03.004
    [20]
    S. Z. Dai, L. Li, and Z. H. Li., “Modeling vehicle interactions via modified LSTM models for trajectory prediction,” IEEE Access, vol.7, pp.38287–38296, 2019. doi: 10.1109/ACCESS.2019.2907000
    [21]
    X. Wang, J. Q. Wu, Y. L. Gu, et al., “Human-like maneuver decision using LSTM-CRF model for on-road self-driving,” Int. Conf. on Intelligent Transportation Systems, Maui, HI, DOI: 10.1109/ITSC.2018.8569524, 2018.
    [22]
    C. Zhang, J. He, and W. D. Wang, “Gesture recognition technology of traffic police command based on spatial context and temporal characteristics,” Acta Electronica Sinica, vol.48, no.05, pp.966–974, 2020.
    [23]
    G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Journal of Science, vol.313, no.28, pp.504–507, 2006.
    [24]
    S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Journal of Neural Computation, vol.9, no.8, pp.1735–1780, 1997. doi: 10.1162/neco.1997.9.8.1735
    [25]
    D. Jiang, G. Hu, G. Qi, et al., “A fully convolutional neural network-based regression approach for effective chemical composition analysis using near-infrared spectroscopy in cloud,” Journal of Artificial Intelligence and Technology, vol.1, no.1, pp.74–82, 2021.
    [26]
    Y. Xu and T. T. Qiu, “Human activity recognition and embedded application based on convolutional neural network,” Journal of Artificial Intelligence and Technology, Vol.1, No.1, pp.51–60, 2021.
    [27]
    B. Li, S.Y. Liang, L.Y. Tian, et al., “Intelligent aircraft maneuvering decision based on CNN,” International Conference on Computer Science and Application Engineering, Sanya, article no.138, 2019.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(9)

    Article Metrics

    Article views (654) PDF downloads(75) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return