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

LI Bo; LIANG Shiyang; CHEN Daqing; LI Xitong

doi:10.1049/cje.2020.00.075

Volume 31 Issue 1

Jan. 2022

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Article Navigation > Chinese Journal of Electronics > 2022 > 31(1): 107-115

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

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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

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

doi: 10.1049/cje.2020.00.075

1.
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
2.
School of Engineering, London South Bank University, London SE1 0AA, UK

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)

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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

Abstract

Abstract

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.
- Air combat maneuver decision-making,
- Dimension reduction,
- Hybrid deep learning network,
- Long short term memory network,
- Stacked sparse auto encoder network,
- Time series data

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References(27)

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