用于时敏目标打击链快速构建的智能方法

doi:10.16182/j.issn1004731x.joss.19-0460

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (2): 346-357.doi: 10.16182/j.issn1004731x.joss.19-0460

用于时敏目标打击链快速构建的智能方法

陆嘉波¹, 程培星¹, 黄羿^2,3,*, 姚进强⁴, 杨雪梦², 马新强^2,3, 刘勇²

1.中国船舶重工集团公司第七一六研究所,江苏连云港 222061;
2.浙江大学智能系统与控制研究所,浙江杭州 310027;
3.重庆文理学院大数据智能计算与可视化研究所,重庆 402160;
4.浙江省交通集团检测科技有限公司,浙江杭州 310030

收稿日期:2019-08-29 修回日期:2020-04-17 出版日期:2021-02-18 发布日期:2021-02-20
通讯作者: 黄羿(1976-),女,博士,教授,研究方向为人工智能、计算机仿真。Email: 36931978@qq.com
作者简介:陆嘉波(1979-),男,硕士,高工,研究方向为舰艇指控模型软件。Email: lujiabo_cn_jari@163.com
基金资助:
浙江大学工业控制技术国家重点实验室开放课题(ICT170330,ICT1800413,ICT1900358),浙江省重点研发计划(2019C01004),重庆市发改委重大产业技术研发项目(2018148208),重庆英才创新创业示范团队(CQYC201903167),广东省重点研发计划(2019B010120001)

An Intelligent Method for Rapid Construction of Time Sensitive Target Strike Chain

Lu Jiabo¹, Cheng Peixing¹, Huang Yi^2,3,*, Yao Jinqiang⁴, Yang Xuemeng², Ma Xinqiang^2,3, Liu Yong²

1. 716 Research Institute of China Shipbuilding Industry Corporation, Lianyungang 222061, China;
2. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China;
3. Institute of Intelligent Computing and Visualization based on Big Data, Chongqing University of Arts and Sciences, Chongqing 402160, China;
4. Zhejiang Transportation Testing Technology Co., Ltd, Hangzhou 310030, China

Received:2019-08-29 Revised:2020-04-17 Online:2021-02-18 Published:2021-02-20

摘要/Abstract

摘要： 为提高战斗中时敏打击链的构建效率,优化资源分配,实现一种快速构建时敏打击链的智能方法,提出将模拟退火算法与遗传算法运用于快速构建时敏打击链。仿真实验表明,可以在短时间内完成时敏目标打击优先级排序、传感器平台-目标配对和武器平台-目标配对,并在目标的时间窗口内摧毁目标。优化了传感器和武器的使用,弥补了构建时敏打击链的局限性,提高了对时敏目标的打击能力,对打击敌人和提高部队作战效能具有重大的军事效益。

关键词: 时敏目标, 模拟退火算法, 遗传算法, 时间窗口

Abstract: In order to improve the construction efficiency of the time sensitive hit chain, optimize the resource allocation in combat, and realize an intelligent method for quickly constructing the time-sensitive strike chain, the simulated annealing algorithm and genetic algorithm are adopted to build a time sensitive hit chain. Simulation experiments show that the method can complete the time sensitive target strike priority sorting, sensor platform-target pairing and weapon platform-target pairing in a short time, and destroy the target within the target time window. The proposed method optimizes the application of sensors and weapons, compensates for the limitations of constructing time-sensitive strike chains, and improves the ability to strike time-sensitive targets, which has significant military benefits for striking the enemy and improving the operational effectiveness of the force.

Key words: time sensitive target, simulated annealing algorithm, genetic algorithm, time window

中图分类号:

TP391.9

陆嘉波, 程培星, 黄羿, 姚进强, 杨雪梦, 马新强, 刘勇. 用于时敏目标打击链快速构建的智能方法[J]. 系统仿真学报, 2021, 33(2): 346-357.

Lu Jiabo, Cheng Peixing, Huang Yi, Yao Jinqiang, Yang Xuemeng, Ma Xinqiang, Liu Yong. An Intelligent Method for Rapid Construction of Time Sensitive Target Strike Chain[J]. Journal of System Simulation, 2021, 33(2): 346-357.

参考文献

[1] 张开德, 赵书斌. 时敏打击指挥控制技术初探[J]. 指挥控制与仿真, 2006, 28(2): 1-5.
Zhang Kaide, Zhao Shubin.The Command and Control Technology of Time Critical Strike[J]. Command Control and Simulation, 2006, 28(2): 1-5.
[2] Li X Y, Zhou D Y, Pan Q, et al.Weapon-target Assignment Problem by Multiobjective Evolutionary Algorithm Based on Decomposition[J]. Complexity (S1076-2787), 2018, 2018: 1-19.
[3] Chang T, Kong D, Hao N, et al.Solving the Dynamic Weapon Target Assignment Problem by an Improved Artificial Bee Colony Algorithm with Heuristic Factor Initialization[J]. Applied Soft Computing, 2018, 70: 845-863.
[4] 王圣东. 时敏目标协同打击链的构建[J]. 指挥控制与仿真, 2013, 35(5): 43-46.
Wang Shengdong, Cooperative Engage Link Structuring Method Aiming at Time Critical Target Striking[J]. Command Control and Simulation, 2013, 35(5): 43-46.
[5] 周海瑞, 刘小毅. 美军联合火力机制及其指挥控制系统[J]. 指挥信息系统与技术, 2018, 9(1): 8-17.
Zhou Hairui, Liu Xiaoyi.U.S. Military joint fire Mechanism and its Command and Control Systems[J]. Command Information System and Technology, 2018, 9(1): 8-17.
[6] Dirik N, Hall S N, Moore J T, et al.Maximizing Strike Aircraft Planning Efficiency for a Given Class of Ground Targets[J]. Optimization Letters (S1862-4480), 2015, 9(8): 1729-1748.
[7] Qin H, Fan P, Tang H, et al.An Effective Hybrid Discrete Grey Wolf Optimizer for the Casting Production Scheduling Problem with Multi-objective and Multi-constraint[J]. Computers & Industrial Engineering (S0360-8352), 2019, 128: 458-476.
[8] Weaver P.Development and Evaluation of an Automated Decision Aid for Rapid Re-tasking of Air Strike Assets in Response to Time Sensitive Targets[D]. Monterey: Thesis, Naval Postgraduate School, Monterey, CA. 2004: 2-3.
[9] Sauter J, Matthews R, Robinson J, et al.Swarming Unmanned Air and Ground Systems for Surveillance and Base Protection[C]// AIAA Infotech@Aerospace Conference and AIAA Unmanned Unlimited Conference. Seattle: American Institute of Aeronautics and Astronautics, 2009: 1850.
[10] Kline A, Ahner D, Hill R.The Weapon-target Assignment Problem[J]. Computers & Operations Research (S0305-0548), 2019, 105: 226-236.
[11] 刘钢, 廖守亿, 于功健, 等. 时敏目标打击作战信息链仿真研究[J]. 信息通信, 2016(10): 76-79.
Liu Gang, Liao Shouyi, Yu Gongjian, et al.Simulation Research of Time-sensitive Target Striking Information Chain[J]. Information & Communications, 2016(10): 76-79.
[12] Van Laarhoven P J M, Aarts E H L. Simulated Annealing: Theory and Applications[M] Berlin: Springer, Dordrecht, 1987.
[13] BräYsy O, Dullaert W, Hasle G, et al. An Effective Multi Restart Deterministic Annealing Metaheuristic for the Fleet Size and Mix Vehicle-Routing Problem with Time Windows[J]. Transportation Science (S0041-1655), 2008, 42(3): 371-386.
[14] Anderson J A, Irrgang M E, Glotzer S C, et al.Scalable Metropolis Monte Carlo for Simulation of Hard Shapes[J]. Computer Physics Communications (S1100-4655), 2016, 100(204): 21-30.
[15] Chatterjee S, Laudato M, Lynch L A.Genetic Algorithms and Their Statistical Applications: An Introduction[J]. Computational Statistics & Data Analysis, 1996, 22(6): 633-651.

用于时敏目标打击链快速构建的智能方法

An Intelligent Method for Rapid Construction of Time Sensitive Target Strike Chain

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