态势感知中的数据融合和决策方法综述  

Review of Date Fusion and Decision-making Methods in Situation Awareness

作　　者：盖伟麟 辛丹[1] 王璐[2] 刘欣[1] 胡建斌[2] 

机构地区：[1]北京大学软件与微电子学院,北京100871 [2]北京大学信息科学技术学院,北京100871

出　　处：《计算机工程》2014年第5期21-25,30,共6页Computer Engineering

摘　　要：在赛博空间态势感知的相关研究中,处理不确定、不精确的多源异构信息是态势认识过程中需要解决的一个重要问题。为正确处理这些信息,提高对态势的认识,使得到的态势更具有正确性、时效性和全局性,研究数据融合方式和决策方式等现存的处理技术并进行综述。数据融合包含贝叶斯网络、D-S证据理论、粗糙集理论、神经网络、隐马尔科夫模型及马尔科夫博弈论等方式,决策方式涵盖认知心理学、逻辑学、风险管理等。研究结果表明,目前的技术焦点呈现多样性,但在态势生成应用及验证方面仍有较大的改进空间。In the research of cyberspace situation awareness, how to deal with uncertain, inaccurate multi-source heterogeneous information is an important problem which needs to be solved in the process of situational understanding. In order to accurately handle with the information, improve the awareness of the situation, make the situation more accuracy, timeliness and overall, the paper reviews the existing technology focus, mainly including data fusion methods and decision-making methods. Data fusion methods mainly includes Bayesian network, D-S evidence theory, rough set theory, neural network, hidden Markov model and Markov game theory methods, and decision-making mainly includes cognitive psychology, logic and risk management methods. Research results show that current technology focuses present diversity, but still has great space for improvement in both the situation generation application and verification.

关 键 词：赛博空间 态势感知 多源异构 数据融合 决策 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]