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

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

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), Article 279. 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