Application Tests of MuDIS in Surface Ship Operation Training Simulation System

doi:10.16182/j.issn1004731x.joss.201812022

Abstract

Abstract: To meet the network communication demands of surface ship operation training simulation system, a multi-layer distributed service data interaction RTI was developed, which supported several system frameworks; and the main functions of HLA and DDS standards were realized. The distributed service with multiple key points was adopted; the data with several frequency response mechanisms and intelligent communication methods were provided; and the active analysis of data communication behavior was obtained. It had error-tolerance and adaptive load balancing capabilities. The MuDIS tests on data control, real-time data communication, time advance request delay, error-tolerance performance and so on were carried out for the most critical capability of the network operation of the surface ship operation training simulation system. The test results showed that MuDIS was applicable for multi-layer complicated system simulation.

Key words: MuDIS, multi-layer distributed service, operation training simulation system, application tests

CLC Number:

TP391.9

Xu Jing, Xiong Zhengxiang, Zhang Yongsheng, Xu Ming. Application Tests of MuDIS in Surface Ship Operation Training Simulation System[J]. Journal of System Simulation, 2018, 30(12): 4668-4676.

References

[1] 姚益平. 高性能分布式交互仿真运行支撑平台关键技术研究[D]. 长沙: 国防科技大学, 2003.
Yao Yiping.Research on the Key Technologies of High Performance Run Time Infreastructure of Distributed Interactive Simulation[D]. Changsha: National Uniersity of Defense Technology, 2003.
[2] 陈显锋, 裘丽华, 王占林. 分布式综合仿真平台软件结构设计[J]. 计算机仿真, 2003, 20(1): 94-97.
Chen Xianfeng, Qiu Lihua, Wang Zhanlin.The Software Structure Design for a Distributed Integrated Simulaion Platform[J]. Computer Simulation, 2003, 20(1): 94-97.
[3] 韩超, 郝建国, 黄健, 等. HLA 多联邦互联工具 KD-FBT 的研究与实现[J]. 系统仿真学报, 2006, 18(11): 3118-3122.
Han Chao, Hao Jianguo, Huang Jian, et al.Research and Implementation of the software KD-FBT for Interconnecting HLA Multiple Federations[J]. Journal of System Simulation, 2006, 18(11): 3118-3122.
[4] 梁洪波, 柳林, 姚益平, 等. 高性能RTI自适应通信机制研究[J]. 国防科技大学学报, 2012, 34(3): 148-153.
Liang Hongbo, Liu Lin, Yao Yiping, et al.Research on self-adaptive communication mechanism for high performance RTI[J]. Journal of National University of Defense Technology, 2012, 34(3): 148-153.
[5] 王卫青, 华祖耀. 基于Agent体系结构的HLA联邦成员设计与实现[J]. 计算机仿真, 2006, 23(7): 142-145.
Wang Weiqing, Hua Zuyao.Design and Realization of Federate in HLA based on Agent Architecture[J]. Computer Simulation, 2006, 23(7): 142-145.
[6] 杨乐平, 陈钦, 朱彦伟. HLA 框架下的Agent与 XML 应用研究[J]. 系统仿真学报, 2007, 19(5): 1045-1048.
Yang Leping, Chen Qin, Zhu Yanwei.Study on application of Agent and XML in the HLA[J]. Journal of System Simulation, 2007, 19(5): 1045-1048.
[7] 高志年, 邢汉承. 智能仿真支撑环境H-MAS[J]. 东南大学学报. 2002, 32(2): 197-201.
Gao Zhinian, Xing Hancheng.H-MAS: Intelligent Simulation Supporting Environment[J]. Journal of Southeast University, 2002, 32(2): 197-201.
[8] 周平, 苏银科, 沈超. 基于DDS的分布式数字仿真系统设计与实现[J]. 系统仿真学报, 2014, 26(8): 1678-1691.
Zhou Ping, Su Yinke, Shen Chao.Design and Implementation of Distributed Simulation System Based on DDS[J]. Journal of System Simulation, 2014, 26(8): 1678-1691.
[9] 袁刚, 许文腾. 武器平台仿真级中多帧速混合时间推进方法[J]. 系统仿真技术, 2015, 27(2): 172-175.
Yuan Gang, Xu Wenteng.The methods of Multi-flames Mixed Time Advance of Weapon Platform Level Simulatioin[J]. Journal of System Simulation Technology, 2015, 27(2): 172-175.
[10] 李铁, 康凤举, 邹文荫. 基于消息的分布式仿真支撑环境的研究[J]. 系统仿真学报, 2011, 23(8): 1614-1617.
Li Tie, Kang Fengju, Zou Wenmeng.Research on Message Based Distribute Simulation Runtime Environment[J]. Journal of System Simulation, 2011, 23(8): 1614-1617.
[11] 张婷, 王琪. HLA/RTI 仿真平台的数据分发管理[J]. 计算机系统应用, 2015, 24(4): 223-227.
Zhang Ting, Wang Qi.Data Distribution Management of Flight Simulation Based on HLA/RTI[J]. Journal of Computer System Application, 2015, 24(4): 223-227.

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