摘要
目标毁伤效果评估能够为作战决策提供可信依据,是联合作战不可或缺的关键环节。结合贝叶斯网络与云模型形成云贝叶斯网络,建立基于云贝叶斯网络的预警雷达毁伤效果评估模型,并对各类指标体系变量进行云模型转换;针对传统专家经验法推导条件概率的缺陷,采用证据理论与层次分析法确定各节点的条件概率值;将变量输入云贝叶斯网络,推理得到预警雷达属于各个毁伤等级的概率,运用于对敌反导体系预警雷达系统实施攻击的仿真计算中,并与贝叶斯网络法、模糊综合评判法、云重心评判法、云贝叶斯网络法进行有效性对比。仿真结果表明,基于证据理论层次分析法的云贝叶斯网络无论是计算合理性还是计算准确度方面均有一定程度的提高,是一种能够合理有效地进行目标毁伤效果评估的分析方法。
Target damage evaluation can provide a credible basis for combat decision-making and is an indispensable key link in joint operations.A cloudy Bayesian network-based early warning radar damage evaluation model is established combining Bayesian network and cloud model to form a cloudy Bayesian network,and the cloud model is converted for various indicator system variables.For the defects of the traditional expert experience method in deriving the conditional probability,the dempster-shafer/analytic hierarchy process (DS/AHP) is used to determine the conditional probability value of each node.The variables are input into the cloudy Bayesian network,and the probability that the early warning radar belongs to each damage level is inferred.The proposed method is applied to the simulation calculation of the attack on the early warning radar system in the enemy's anti-missile system,and the effectiveness of cloudy Bayesian, network is compared with those of Bayesian netwok, fuzzy comprehensive evaluation method and cloud barysenter evaluation method.The simulated results show that cloudy bayesian network based on dempster-shafer/analytic hierarchy process method has a certain degree of improvement in terms of calculation rationality and calculation accuracy.It is an analysis method that can reasonably and effectively evaluate the damage effect of target.
作者
邓力源
杨萍
刘卫东
汪江鹏
DENG Liyuan;YANG Ping;LIU Weidong;WANG Jiangpeng(Foundation Department, Rocket Force University of Engineering, Xi'an 710025, Shaanxi, China)
出处
《兵工学报》
EI
CAS
CSCD
北大核心
2022年第4期814-825,共12页
Acta Armamentarii
基金
全军军事类研究生资助项目(JY2018A046)。
关键词
云模型
贝叶斯网络
证据理论与层次分析法
毁伤效果评估
cloud model
Bayesian network
dempster-shafer/analytic hierarchy process
damage effect evaluation
