基于博弈论的无人艇探查策略研究

郭苗; 徐琰锋; 陈铢蕾

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

基于博弈论的无人艇探查策略研究

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

上海船舶电子设备研究所, 上海, 201108

详细信息

作者简介:
郭苗：郭　苗(1995-), 女, 硕士, 助理工程师, 主要研究方向为无人平台探查策略研究

中图分类号: U674.941; TJ630.34
计量
- 文章访问数: 303
- HTML全文浏览量: 67
- PDF下载量: 46
- 被引次数: 0
出版历程
- 收稿日期: 2022-08-01
- 修回日期: 2022-11-02
- 录用日期: 2022-11-22
- 网络出版日期: 2022-12-13

Research on Unmanned Surface Vehicle Detection Strategy Based on Game Theory

Shanghai Marine Electronic Equipment Research Institute, Shanghai 201108, China

摘要

摘要: 针对水下小目标采取规避动作形成跟踪丢失, 不易探查, 进而导致安防系统虚警率较高的问题, 文中在固定式声呐检测到目标并不断获取相关数据的前提下, 考虑搭载图像声呐的无人艇对目标进行近距离查证的情景。通过固定式声呐获取目标的轨迹数据, 而后采用粒子滤波方法对轨迹数据进行预测。随后建立我方无人艇与敌方目标的博弈模型, 根据敌方目标每一时刻的动作和模型中的支付函数, 选择对我方最有利的决策, 以此形成双方的对抗过程。最后通过数值仿真得到无人艇的目标点和探查策略, 并利用试验数据验证目标点的准确性, 方便无人艇成功探查目标。文中研究可为无人系统探查小目标提供理论依据。
- 无人艇 /
- 粒子滤波 /
- 博弈论 /
- 图像声呐 /
- 探查策略
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

HTML全文

图 1 角度变化示意图

Figure 1. Schematic diagram of angle change

下载: 全尺寸图片幻灯片

图 2 无人艇可探测范围

Figure 2. Detectable range of USV

下载: 全尺寸图片幻灯片

图 3 探查策略图

Figure 3. Diagram of detection strategies

下载: 全尺寸图片幻灯片

图 4 探查策略与夹角对应图

Figure 4. Diagram of detection strategy and angle

下载: 全尺寸图片幻灯片

图 5 目标轨迹图

Figure 5. Diagram of target trajectory

下载: 全尺寸图片幻灯片

图 6 粒子滤波预测与实际轨迹图(70 s)

Figure 6. Diagram of particle filter prediction and actual trajectory (70 s)

下载: 全尺寸图片幻灯片

图 7 粒子滤波预测与实际轨迹图(150 s)

Figure 7. Diagram of particle filter prediction and actual trajectory (150 s)

下载: 全尺寸图片幻灯片

图 8 双方博弈图(120 s内)

Figure 8. Game image between both sides(within 120 s)

下载: 全尺寸图片幻灯片

图 9 利用新数据预测博弈得到的结果

Figure 9. The predicted results of the game based on new data

下载: 全尺寸图片幻灯片

图 10 实际轨迹与预测轨迹对比图

Figure 10. Comparison between actual trajectory and predicted trajectory

下载: 全尺寸图片幻灯片

参考文献(12)

[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