系统工程与电子技术 ›› 2023, Vol. 45 ›› Issue (4): 1049-1071.doi: 10.12305/j.issn.1001-506X.2023.04.14
• 系统工程 • 上一篇
李梦杰, 常雪凝, 石建迈, 陈超, 黄金才, 刘忠
收稿日期:
2021-08-19
出版日期:
2023-03-29
发布日期:
2023-03-28
通讯作者:
石建迈
作者简介:
李梦杰(1997—), 男, 硕士研究生, 主要研究方向为武器目标分配Mengjie LI, Xuening CHANG, Jianmai SHI, Chao CHEN, Jincai HUANG, Zhong LIU
Received:
2021-08-19
Online:
2023-03-29
Published:
2023-03-28
Contact:
Jianmai SHI
摘要:
武器目标分配问题是指挥控制与任务规划领域的关键难点之一, 也是军事运筹领域的基础研究课题。经过多年研究, 武器目标分配问题在陆海空天电等领域都得到了广泛研究, 涌现出了大量模型和算法。系统梳理武器目标分配问题的典型作战样式、建模方法、求解算法和实验验证, 掌握当前该领域的研究现状, 在此基础上, 结合智能化、无人化战争带来的新挑战, 分析武器目标分配的发展趋势, 为后续研究提供参考。
中图分类号:
李梦杰, 常雪凝, 石建迈, 陈超, 黄金才, 刘忠. 武器目标分配问题研究进展: 模型、算法与应用[J]. 系统工程与电子技术, 2023, 45(4): 1049-1071.
Mengjie LI, Xuening CHANG, Jianmai SHI, Chao CHEN, Jincai HUANG, Zhong LIU. Developments of weapon target assignment: models, algorithms, and applications[J]. Systems Engineering and Electronics, 2023, 45(4): 1049-1071.
表1
典型作战样式的问题特点"
典型作战样式 | 问题特点 | ||
问题规模 | 复杂性 | 时效性 | |
地面防空 | 不同作战场景时, 问题规模涵盖大、中、小等不同范围。 | 早期研究一般简化为标准的WTA问题, 近期研究开始考虑与传感器的协同, 问题复杂性增加较大。 | 拦截目标为弹道导弹时, 时效性要求比较高; 为巡航导弹、飞机等目标时, 时间窗口相对较长, 求解时效性可适当放宽。 |
舰艇防空 | 单舰防空时通常为小规模; 舰艇编队防空通常为中、大规模。 | 单舰防空过程中武器之间以及武器与传感器的协同约束较为复杂, 编队防空时还要增加舰艇间的协同, 一般很难建立标准的数学规划模型。 | 舰艇防空在作战中, 来袭目标一般速度很快, 对问题求解的时效性要求非常高。 |
空中拦截 | 空中拦截以舰载机或战斗机拦截目标为主, 问题规模通常为中、小规模。 | 通常情况下空中拦截问题的求解复杂性一般。 | 空中拦截具有高速度移动、时间窗口较短, 时效性通常较高。 |
协同空战 | 协同空战通常以编队形式协同作战, 甚至是以大规模无人机等蜂群作战为代表, 问题规模通常较大。 | 问题求解复杂性通常较高, 需要处理中、大规模的WTA。 | 协同空战同样具有高速度移动、作战节奏快等特点, 时效性通常较高。 |
地对地打击 | 与作战规模相关, 以中、大规模为主, 少量小规模问题。 | 通常基于标准WTA数学规划模型进行扩展, 大规模问题求解复杂性较大。 | 通常有较长时间开展战前规划, 时效性要求不高。 |
空对地打击 | 与作战规模相关, 以中、小规模为主, 少量大规模问题。 | 通常基于标准WTA数学规划模型进行扩展, 大规模问题或考虑敌方防空因素时求解复杂性较大。 | 战前离线规划为主, 时效性要求不高; 动态在线规划时, 时效性要求高。 |
反舰 | 反舰通常涉及到几个波次打击的问题, 舰艇编队数量有限, 无论反单舰还是反舰艇编队, 问题规模通常不是太大。 | 反舰问题的静态规划复杂性不高, 但是考虑敌方拦截对抗时, 复杂性会急剧增大。 | 反舰作战节奏快, 对战机的把握要求高, 一般需要在极短时间内完成求解。 |
装甲对战 | 现代战争, 装甲对战以小规模为主, 特殊场景才会出现大规模集团式的装甲对抗。 | 小规模的装甲对战, 问题求解复杂性相对较低。 | 装甲机动性强, 近距离对抗时, 求解时效性要求较高。 |
联合火力打击 | 联合火力打击问题规模通常为中、大规模。 | 考虑武器之间协同或武器与传感器协同时, 问题复杂性通常较大。 | 战前静态规划, 时间比较宽松; 战中在线规划, 时效性要求较高。 |
表2
建模方法的特点及其重点应用领域"
建模方法 | 特点 | 重点应用领域 |
数学规划 | 包括线性整数规划、非线性整数规划、0-1整数规划等, 模型多样, 形式化严谨, 便于数学求解, 应用领域广。 | 适用于进攻和作战中的所有典型作战样式, 尤其是便于求解大规模问题。 |
博弈论 | 便于分析双方对抗策略影响, 数学描述严谨。 | 包含小规模动态对抗作战场景的作战样式。 |
图论 | 一般需要对WTA问题进行转化与简化, 建立在数学规划模型基础上, 不适合求解规模较大的问题。 | 武器和目标相对简单, 能够建模为基础WTA问题的作战场景。 |
动态规划 | 便于分析多阶段分配过程中动态不确定因素的影响, 一般只能求解中小规模问题。 | 作战过程中存在多个阶段的作战场景。 |
多智能体建模 | 强调人工智能的应用, 特别是机器学习和强化学习的应用。 | 时效性要求高、需要进行分布式协同的作战场景。 |
网络流 | 需要对WTA问题进行转化与简化, 建立在数学规划模型基础之上, 便于求解大规模问题。 | 武器和目标相对简单、不需要考虑协同或额外约束的作战场景。 |
表3
算法特点及其适用问题"
算法 | 优点 | 局限性 | 适用问题 |
精确算法 | 能够求解出精确最优解, 便于理解与实现。 | 难以求解大规模问题; 一般需要问题的精确数学模型。 | 时效性要求不高, 约束简单的中、小规模问题。 |
元启发式算法 | 算法框架便于实现, 可以对解空间进行大范围搜索, 能够求解大规模问题。 | 求解时间较长, 算法性能不够稳定。 | 时效性要求不高的大规模问题。 |
基于规则的 启发式算法 | 充分利用领域知识, 求解时间短、效率高。 | 解的质量依赖于规则的设定; 一般只能得到局部最优解。 | 时效性要求高、约束复杂的中、小规模问题。 |
机器学习算法 | 持续学习和进化; 开源工具支持完备; 算法通用性较好; 训练好的模型执行效率高。 | 训练数据要求高; 可解释性差; 依赖强大算力; 解空间维数灾难。 | 作战规则明确、时效性要求高的中、小规模问题。 |
表5
测试数据的代表性文献及其获取方式"
测试数据 | 代表文献 | 获取方式 |
想定数据 | Wacholder[ | |
Li等[ | ||
Xin等[ | ||
Wang等[ | ||
随机数据 | Xin等[ | |
Lu等[ | ||
Sonuc[ |
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