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Information entropy-based intention prediction of aerial targets under uncertain and incomplete information

Zhou, Tongle; Chen, Mou; Wang, Yuhui; He, Jianliang; Yang, Chenguang

Information entropy-based intention prediction of aerial targets under uncertain and incomplete information Thumbnail


Authors

Tongle Zhou

Mou Chen

Yuhui Wang

Jianliang He



Abstract

© 2020 by authors. To improve the effectiveness of air combat decision-making systems, target intention has been extensively studied. In general, aerial target intention is composed of attack, surveillance, penetration, feint, defense, reconnaissance, cover and electronic interference and it is related to the state of a target in air combat. Predicting the target intention is helpful to know the target actions in advance. Thus, intention prediction has contributed to lay a solid foundation for air combat decision-making. In this work, an intention prediction method is developed, which combines the advantages of the long short-term memory (LSTM) networks and decision tree. The future state information of a target is predicted based on LSTM networks from real-time series data, and the decision tree technology is utilized to extract rules from uncertain and incomplete priori knowledge. Then, the target intention is obtained from the predicted data by applying the built decision tree. With a simulation example, the results show that the proposed method is effective and feasible for state prediction and intention recognition of aerial targets under uncertain and incomplete information. Furthermore, the proposed method can make contributions in providing direction and aids for subsequent attack decision-making

Citation

Zhou, T., Chen, M., Wang, Y., He, J., & Yang, C. (2020). Information entropy-based intention prediction of aerial targets under uncertain and incomplete information. Entropy, 22(3), https://doi.org/10.3390/e22030279

Journal Article Type Article
Acceptance Date Feb 26, 2020
Online Publication Date Feb 28, 2020
Publication Date Mar 1, 2020
Deposit Date Mar 29, 2020
Publicly Available Date Mar 31, 2020
Journal Entropy
Electronic ISSN 1099-4300
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 22
Issue 3
Article Number 279
DOI https://doi.org/10.3390/e22030279
Keywords General physics and astronomy; state prediction; LSTM networks; intention recognition; decision tree; data missing; interval-valued
Public URL https://uwe-repository.worktribe.com/output/5827679

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