@inproceedings{cb5a937834cb47febd1580a6f1d0e249,
title = "UAV Air Combat Algorithm Based on Bayesian Probability Model",
abstract = "In recent years, the comprehensive capability of unmanned aerial vehicles (UAVs) has been significantly improved, and the participation of UAV in modern wars has gradually become a reality. The maneuvering decision-making for UAV air combat is an intelligent process. In this paper, a one-to-one air combat algorithm based on Bayesian probability model and influence diagram is proposed. The element of air combat is analyzed with the help of influence diagram. In the environment of one-to-one air combat, an air combat assessment model is proposed based on Bayesian theory to predict air combat situation. Then, the UAV makes autonomous decisions based on the current air combat situation prediction results. The simulation results validate the effectiveness of the proposed algorithm.",
keywords = "Air combat decision-making, Bayesian probabilistic model, Influence diagram",
author = "Chubing Guo and Jianing Zhang and Jinwen Hu and Yongping Zhang and Zengfa Dou and Jingyuan Liang",
note = "Publisher Copyright: {\textcopyright} 2023, Beijing HIWING Sci. and Tech. Info Inst.; International Conference on Autonomous Unmanned Systems, ICAUS 2022 ; Conference date: 23-09-2022 Through 25-09-2022",
year = "2023",
doi = "10.1007/978-981-99-0479-2_292",
language = "英语",
isbn = "9789819904785",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "3176--3185",
editor = "Wenxing Fu and Mancang Gu and Yifeng Niu",
booktitle = "Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022",
}