空战场多分支态势仿真生成方法  

Multi-branch Situation Simulation Generation Method for Air Battlefield

作　　者：唐剑[1] 张杰勇[1] 焦志强[1] 刁联旺[2] 李玉萍 TANG Jian;ZHANG Jieyong;JIAO Zhiqiang;DIAO Lianwang;LI Yuping(Information and Navigation College,Air Force Engineering University,Xi′an 710077,China;Science and Technology on Information System Engineering Laboratory,Nanjing 210007,China)

机构地区：[1]空军工程大学信息与导航学院,西安710077 [2]信息系统工程重点实验室,南京210007

出　　处：《指挥信息系统与技术》2019年第3期11-17,共7页Command Information System and Technology

基　　金：国家自然科学基金面上(61573017和61703425)资助项目

摘　　要：针对空战场态势要素多、影响因素复杂和不确定性强的特点,提出了空战场环境下多分支态势仿真生成方法。该方法将空战场态势预测分为战场实时态势情报接收、敌方目标分群和多分支态势生成3个模块。首先,对态势信息进行收集、格式化及分类存储,为后续多分支态势生成奠定基础;然后,对敌方目标多维属性信息进行深层次抽象,从而将众多敌方目标分解成多个战术编队,达到降解态势分支生成复杂度的目的;最后,结合深度神经网络和贝叶斯网络等方法生成主分支及旁路分支,并对多分支态势进行规范化表征。通过案例分析可见,该方法可提前预测战场态势信息,为指挥员快速、科学地决策提供支撑。Aimed at the air battlefield situation features of many factors,complex influencing factors and strong uncertainty,a multi-branch situation simulation generation method is proposed.The method is divided the air battlefield situation prediction into three modulars,including the battlefield real-time situation intelligence collection modular,the hostile target clustering modular and multi-branch situation generation modular.Firstly,the situation information is collected,formatted and stored in classifications,and the foundation is established for the future multi-branch situation generation.Then,the multi-dimensional attribute information of hostile targets is deeply abstracted.Thus,the hostile targets are classified into multiple tactical formations to achieve the goal of degrading the complexity of the situation branch generation.Finally,combined with the methods of the deep neural network and the Bayesian network,the main branch and bypass branches are generated,and the standardizing multi-branch situation is represented.The case analysis shows that the method can predict the battlefield situation information in advance and provide support for commanders′ quick and scientific decision-makings.

关 键 词：空战场态势预测 多分支态势 深度神经网络 贝叶斯网络 规范化表征 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]