基于因果熵的无人集群对抗评估指标分配方法

doi:10.12305/j.issn.1001-506X.2024.06.21

系统工程与电子技术 ›› 2024, Vol. 46 ›› Issue (6): 2034-2043.doi: 10.12305/j.issn.1001-506X.2024.06.21

• 系统工程 • 上一篇

基于因果熵的无人集群对抗评估指标分配方法

范波¹, 钟季龙¹, 徐丽霞¹, 吕筱璇², 王鹥喆², 刘禹², 侯新文²^,*

1. 军事科学院国防科技创新研究院, 北京 100071
2. 中国科学院自动化研究所, 北京 100190

收稿日期:2023-03-23 出版日期:2024-05-25 发布日期:2024-06-04
通讯作者: 侯新文
作者简介:范波 (1982—), 男, 研究员, 博士, 主要研究方向为智能测试评估
钟季龙 (1990—), 男, 副研究员, 博士, 主要研究方向为智能测试评估
徐丽霞 (1991—), 女, 助理研究员, 博士, 主要研究方向为智能测试评估
吕筱璇 (1998—), 女, 助理工程师, 硕士, 主要研究方向为智能系统评估
王鹥喆 (1996—), 男, 助理工程师, 硕士, 主要研究方向为智能体评估、生成模型
刘禹 (1979—), 男, 研究员, 博士, 主要研究方向为智能决策与评估
侯新文 (1973—), 男, 副研究员, 博士, 主要研究方向为强化学习、智能测试评估
基金资助:
国家自然科学基金(72001213)

Index allocation method for unmanned swarm confrontation evaluation based on causal entropy

Bo FAN¹, Jilong ZHONG¹, Lixia XU¹, Xiaoxuan LYU², Yizhe WANG², Yu LIU², Xinwen HOU²^,*

1. Defense Innovation Institute, Academy of Military Science, Beijing 100071, China
2. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Received:2023-03-23 Online:2024-05-25 Published:2024-06-04
Contact: Xinwen HOU

摘要/Abstract

摘要：

针对传统无人集群对抗评估指标分配方法仅能选出相关性较好的评估指标, 存在指标混淆和虚假相关问题，提出因果熵概念，用于度量评估指标和对抗任务完成效果间的确定性程度, 从而滤除评估指标之间的混淆效应。以仿真环境下的无人集群空地协同围捕试验为例, 通过评估指标因果分配和约简优选出更能反映无人集群对抗能力且相对独立的评估指标。仿真结果表明, 所提方法可以有效地优化评估指标体系, 获取更具代表性的评估指标, 具有一定的普适性。

关键词: 结构因果模型, 因果熵, 指标分配, 指标约简, 冗余度

Abstract:

In response to the problem of index confusion and false correlation in traditional unmanned swarm confrontation evaluation index allocation methods, which can only select indexes with good correlation, causal entropy is innovatively is proposed to measure the certainty degree between the evaluation indexes and the effect of the confrontation task, in order to filter out the confusion among evaluation indexes. Taking the unmanned swarm air-ground collaborative encirclement test in the simulation environment as an example, the evaluation indexes that better reflect the combat capability of the unmanned swarm which are relatively independent are selected and optimized through causal allocation and reduction. The simulation results show that the proposed method can effectively optimize the evaluation index system, obtain more representative evaluation indicators, and has a certain degree of universality.

Key words: structural causal model, causal entropy, index allocation, index reduction, redundancy degree

中图分类号:

范波, 钟季龙, 徐丽霞, 吕筱璇, 王鹥喆, 刘禹, 侯新文. 基于因果熵的无人集群对抗评估指标分配方法[J]. 系统工程与电子技术, 2024, 46(6): 2034-2043.

Bo FAN, Jilong ZHONG, Lixia XU, Xiaoxuan LYU, Yizhe WANG, Yu LIU, Xinwen HOU. Index allocation method for unmanned swarm confrontation evaluation based on causal entropy[J]. Systems Engineering and Electronics, 2024, 46(6): 2034-2043.

图/表 7

图1

图2

表1

无人集群空地协同围捕评估指标体系"

一级评估指标	二级评估指标	三级评估指标	三级评估指标说明
生存性	抗毁性	集群抗毁能力X₁	反映红方受到攻击后的抗毁能力
		集群可靠性X₂	反映红方受到攻击后仍保持原有结构的能力
		集群稳定性X₃	反映红方受到攻击后的稳定程度
	自恢复性	集群自恢复能力X₄	反映红方受到攻击后恢复稳定的能力
适用性	环境适应性	空地协同环境适应能力X₅	反映红方的环境适应度
适用性	任务适应性	空地协同任务适应能力X₆	反映红方实现预期任务的能力
作战效能	作战能力	平均度X₇	反映红方的内部连通能力
		平均路径长度X₈	反映红方的联络支援距离
		谱半径X₉	反映红方各算子之间的联络能力
		自然连通度X₁₀	反映红方的整体稳定程度
		节点度X₁₁	反映与红方直接相连的其他算子的数量
		介数X₁₂	反映红方某个算子的重要程度
		接近度X₁₃	反映红方某个算子到集群中心的接近程度
		聚集系数X₁₄	反映红方某个算子附近的集聚程度
	作战效果	综合对抗能力X₁₅	反映红方在对抗中的综合实力比值
		机动穿插能力X₁₆	反映红方在对抗中机动距离的比值
		目标打击能力X₁₇	反映红方在对抗中的火力打击命中率
		毁伤歼敌能力X₁₈	反映红方在对抗中歼灭蓝方的比例
		战场生存能力X₁₉	反映红方在对抗中的兵力生存比例
		红方地面兵力打击效果X₂₀	反映红方在对抗中地面兵力的打击效果
		红方空中兵力打击效果X₂₁	反映红方在对抗中空中兵力的打击效果
		对蓝方地面兵力打击效果X₂₂	反映红方在对抗中对蓝方地面兵力的打击效果
		对蓝方空中兵力打击效果X₂₃	反映红方在对抗中对蓝方空中兵力的打击效果
	资源代价	地面兵力资源代价X₂₄	反映红方在对抗中的地面兵力资源消耗
		空中兵力资源代价X₂₅	反映红方在对抗中的空中兵力资源消耗
		地面弹药资源代价X₂₆	反映红方在对抗中地面兵力的弹药资源消耗
		空中弹药资源代价X₂₇	反映红方在对抗中空中兵力的弹药资源消耗
	作战效率	红方地面兵力时间效率X₂₈	反映红方地面兵力从发现蓝方兵力到对其发起攻击的时间的综合时效
		红方空中兵力时间效率X₂₉	反映红方空中兵力从发现蓝方兵力到对其发起攻击的时间的综合时效
		对蓝方地面兵力打击时间效率X₃₀	反映红方兵力从发现蓝方地面兵力到对其发起攻击的时间的综合时效
		对蓝方空中兵力打击时间效率X₃₁	反映红方兵力从发现蓝方空中兵力到对其发起攻击的时间的综合时效

表1

表2

图3

图4

图5

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基于因果熵的无人集群对抗评估指标分配方法

Index allocation method for unmanned swarm confrontation evaluation based on causal entropy

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评估指标	因采熵结果	评估指标	因采熵结果
X₁	4.23	X₁₇	4.16
X₂	4.22	X₁₈	4.05
X₃	4.08	X₁₉	4.26
X₄	4.08	X₂₀	4.07
X₅	4.06	X₂₁	4.12
X₆	4.06	X₂₂	4.49
X₇	4.19	X₂₃	4.03
X₈	4.20	X₂₄	4.08
X₉	4.09	X₂₅	4.50
X₁₀	4.15	X₂₆	4.26
X₁₁	4.15	X₂₇	4.01
X₁₂	4.13	X₂₈	4.16
X₁₃	4.01	X₂₉	4.13
X₁₄	4.12	X₃₀	4.22
X₁₅	2.32	X₃₁	4.03
X₁₆	4.14	-	-