系统仿真学报 ›› 2021, Vol. 33 ›› Issue (3): 745-752.doi: 10.16182/j.issn1004731x.joss.19-0572

• 国家安全仿真 • 上一篇    

LVC训练系统中计算机生成兵力生成技术研究

高昂1, 董志明1, 张国辉1, 梁涛2, 郭齐胜1   

  1. 1.陆军装甲兵学院 演训中心,北京 100072;
    2.山东理工大学 管理学院,山东 淄博 255000
  • 收稿日期:2019-10-31 修回日期:2020-03-27 出版日期:2021-03-18 发布日期:2021-03-18
  • 作者简介:高昂(1988-),男,博士生,助理讲师,研究方向为装备作战与保障仿真。E-mail:gaoang370829@sohu.com
  • 基金资助:
    军队科研计划(41405030302,41401020301)

Research on Generation Technology of Computer Generated Force in LVC Training System

Gao Ang1, Dong Zhiming1, Zhang Guohui1, Liang Tao2, Guo Qisheng1   

  1. 1. Army Armored Force Academy Drill and Training Center, Beijing 100072, China;
    2. Department of Management, Shandong University of Technology, Zibo 255000, China
  • Received:2019-10-31 Revised:2020-03-27 Online:2021-03-18 Published:2021-03-18

摘要: LVC训练是实战化条件下装备体系对抗训练的一种有效手段,针对LVC训练系统中,计算机生成兵力难以满足训练需求问题,明确LVC训练与LVC训练系统概念,按照模型与系统的结构组成关系阐述了逻辑靶场实体配置、指挥实体、战斗实体3个不同层次模型相应的建模技术需求。针对具体需求,提出基于复杂网络的逻辑靶场虚实实体配置、基于深度强化学习的分队战术决策建模、基于动态贝叶斯网、遗传神经网络的战术行为参数矫正建模4种计算机生成兵力生成技术。

关键词: 计算机生成兵力, LVC训练, 复杂网络, 深度强化学习, 动态贝叶斯网络, 遗传神经网络

Abstract: LVC training system of the combat equipment under the condition of confrontation is an effective means of training, aiming at the problem that in LVC training system, computer generated forces are difficult to meet the demand of training problems. The concept of LVC training and LVC training system is clarified, according to the relationship between model and system structure, the corresponding modeling technology requirements of three different hierarchical models, namely logical range entity configuration, command entity and combat entity, are expounded. According to the specific requirements, four computer-generated force generation methods are proposed, namely, logical target range virtual and real entity configuration based on complex network, unit tactical decision modeling based on deep reinforcement learning, tactical behavior parameter correction modeling based on dynamic bayesian network and genetic neural network.

Key words: computer generated forces, LVC training, complex networks, deep reinforcement learning, dynamic bayesian network, genetic neural network

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