Research on Unmanned Surface Vehicle Detection Strategy Based on Game Theory

GUO Miao; XU Yan-feng; CHEN Zhu-lei

doi:10.11993/j.issn.2096-3920.2022-0032

Volume 30 Issue 6

Dec 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Unmanned Undersea Systems > 2022 > 30(6): 754-760

GUO Miao, XU Yan-feng, CHEN Zhu-lei. Research on Unmanned Surface Vehicle Detection Strategy Based on Game Theory[J]. Journal of Unmanned Undersea Systems, 2022, 30(6): 754-760. doi: 10.11993/j.issn.2096-3920.2022-0032

Citation:

GUO Miao, XU Yan-feng, CHEN Zhu-lei. Research on Unmanned Surface Vehicle Detection Strategy Based on Game Theory[J]. Journal of Unmanned Undersea Systems, 2022, 30(6): 754-760. doi: 10.11993/j.issn.2096-3920.2022-0032

Citation:

PDF( 1449 KB)

Research on Unmanned Surface Vehicle Detection Strategy Based on Game Theory

doi: 10.11993/j.issn.2096-3920.2022-0032

Shanghai Marine Electronic Equipment Research Institute, Shanghai 201108, China

Received Date: 2022-08-01
Accepted Date: 2022-11-22
Rev Recd Date: 2022-11-02

Available Online: 2022-12-13

Abstract

Abstract

Owing to the evasive action of small underwater targets, they are lost and difficult to detect, which leads to a high false alarm rate of the security system. Based on stationary sonar detection of the target and continuously obtaining relevant data, this study considers the scene of an unmanned surface vehicle (USV) that carries the image sonar to verify the target at a short distance. First, the trajectory data of the target are obtained using stationary sonar, and then the particle filter method is used to predict the trajectory data. Then, a game theory model between our USV and the enemy target is established. According to the action of the enemy target at every moment and the payment function in the model, our USV chooses the most favorable decision to form the confrontation process of both sides. Finally, the target points and detection strategies of the USV are obtained through a numerical simulation. The accuracy of the target point was verified by the test data, which is convenient for the USV to successfully detect the target. The results of this study provide a theoretical basis for unmanned systems to detect small targets.
- unmanned surface vehicle,
- particle filter,
- game theory,
- image sonar,
- detection strategy

FullText(HTML)

References(12)

References

[1]	杜方键, 张永峰, 张志正, 等. 水中无人作战平台发展现状与趋势分析[J]. 科技创新与应用, 2019(27): 1-9. Du Fang-Jian, Zhang Yong-Feng, Zhang Zhi-Zheng, et al. Development Status and Trend Analysis of Underwater Unmanned Combat Platform[J]. Technology Innovation and Application, 2019(27): 1-9.
[2]	祝超, 蔡颂, 王志强. 水下无人平台应召搜潜效能研究[C]//水下无人系统技术高峰论坛. 西安: 水下无人系统学报编辑部, 2018.
[3]	周亮, 周浩, 张勇明. 美军水下无人作战系统开发现状及启示[J]. 中阿科技论坛(中英文), 2021(10): 12-14. Zhou Liang, Zhou Hao, Zhang Yong-ming. Development Status and Enlightenment of Underwater Unmanned Combat System of the U.S.Army[J]. China-Arab States Science and Technology Forum, 2021(10): 12-14.
[4]	冯振宇, 彭倍, 王刚. 基于图神经网络技术的水下无人系统智能决策研究[J]. 舰船科学技术, 2020, 42(23): 63-66. doi: 10.3404/j.issn.1672-7649.2020.12.012 Feng Zhen-Yu, Peng Bei, Wang Gang. Research on Intelligent Decision of Underwater Unmanned System Based on Graph Neural Network Technology[J]. Ship Science and Technology, 2020, 42(23): 63-66. doi: 10.3404/j.issn.1672-7649.2020.12.012
[5]	张法帅, 李宝安, 阮子涛. 基于深度强化学习的无人艇航行控制[J]. 计测技术, 2018, 38(z1): 207-211. Zhang Fa-Shuai, Li Bao-An, Ruan Zi-Tao. Navigation Control of Unmanned Craft Based on Deep Reinforcement Learning[J]. Metrology & Measurement Technology, 2018, 38(z1): 207-211.
[6]	马晶, 刘鹏, 仵钇征, 等. 深度强化学习应用于海战场多智能体对抗问题研究[J]. 舰船科学技术, 2021, 43(S1): 123-129. Ma Jing, Liu Peng, Wu Yi-Zheng, et al. Research on the Application of Deep Reinforcement Learning to Multi-Agent Confrontation in Sea Battlefield[J]. Ship Science and Technology, 2021, 43(S1): 123-129.
[7]	刘斌, 吕良栋. 基于BP网络的强化学习在作战仿真中的应用[C]//中国电子学会电子系统工程分会第五届军事信息软件与仿真学术研讨会论文集. 徐州: 中国电子学会, 2006.
[8]	付超, 杨善林. 基于博弈论的多无人机协同作战仿真系统[J]. 系统仿真学报, 2009, 21(9): 2591-2594. doi: 10.16182/j.cnki.joss.2009.09.055 Fu Chao, Yang Shan-Lin. Multi UAV Cooperative Combat Simulation System Based on Game Theory[J]. Journal of System Simulation, 2009, 21(9): 2591-2594. doi: 10.16182/j.cnki.joss.2009.09.055
[9]	惠一楠, 朱华勇, 沈林成. 无人机攻防对抗不完全信息动态博弈方法研究[J]. 兵工自动化, 2009, 28(1): 4-7. doi: 10.3969/j.issn.1006-1576.2009.01.002 Hui Yi-Nan, Zhu Hua-Yong, Shen Lin-Cheng. Research on Dynamic Game Method of UAV Attack Defense Confrontation with Incomplete Information[J]. Ordnance Industy Automation, 2009, 28(1): 4-7. doi: 10.3969/j.issn.1006-1576.2009.01.002
[10]	赵明明. 不确定信息下多无人机空战动态博弈策略研究[D]. 沈阳: 沈阳航空航天大学, 2013.
[11]	李迎春, 程建博, 于尧. 基于博弈论的无人机战场攻防策略求解模型[J]. 兵器装备工程学报, 2017, 38(6): 70-72. doi: 10.11809/scbgxb2017.06.015 Li Ying-Chun, Cheng Jian-Bo, Yu Yao. Solution Model of UAV Battlefield Attack and Defense Strategy Based on Game Theory[J]. Journal of Ordance Equipment Engineering, 2017, 38(6): 70-72. doi: 10.11809/scbgxb2017.06.015
[12]	姜鑫, 杜正军, 王长春, 等. 不确定性环境下的多阶段军事对抗决策方法[J]. 系统工程理论与实践, 2013, 33(8): 2163-2168. doi: 10.3969/j.issn.1000-6788.2013.08.034 Jiang Xin, Du Zheng-Jun, Wang Chang-Chun, et al. Multi-stage Military Confrontation Decision Making Method in Uncertain Environment[J]. Systems Engineering-Theory & Practice, 2013, 33(8): 2163-2168. doi: 10.3969/j.issn.1000-6788.2013.08.034