摘要
针对海上编队防空目标威胁评估过程中样本数据量较少且易缺失、已有评估方法过多依赖专家经验以及难以进行时间序列上动态评估的问题,提出了基于约束参数学习的动态贝叶斯网络(dynamic Bayesian networks,DBN)威胁评估方法。采用AR(p)模型预测时间序列上的缺失数据,从而获得完备的小数据集样本;在此基础上,根据专家经验构建合理的参数约束模型;进一步利用贝叶斯估计进行参数学习;将学习得到的参数代入DBN中,推理求出威胁评估结果;引入效用理论对威胁评估结果进行排序。仿真实验表明该评估方法在小样本数据缺失状态下目标威胁评估的结果合理,准确性高。
On threat assessment of marine formation air defense target, the sample data are less and easy to be missing, and existing evaluation methods rely too much on expertise and are difficult to carry out dynamic assessment on time series. In order to solve these problems, a threat assessment method based on dynamic Bayesian networks (DBN) of constraint parameter learning is proposed. The AR( p ) model is used to predict the missing data on the time series, so as to obtain a complete sample of small data sets. On the basis of this, a reasonable parameter constraint model is constructed according to expertise experience. The parameter learning is carried out by Bayesian estimation under the parameter constraint model. The learning parameters are brought into DBN to get the threat evaluation results. The utility theory is introduced to sort the results of threat assessment. The simulation results show that the evaluation method is reasonable and more accurate in the condition of absence of small sample data.
作者
孙海文
谢晓方
孙涛
张龙杰
SUN Haiwen;XIE Xiaofang;SUN Tao;ZHANG Longjie(Naval Aeronautical University, Yantai 264001, China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2019年第6期1300-1308,共9页
Systems Engineering and Electronics
基金
中国博士后科学基金(2013T60923)资助课题
关键词
动态贝叶斯网络
约束模型
参数学习
AR(P)模型
小样本数据缺失
威胁评估及排序
效用理论
dynamic Bayesian networks (DBN)
constraint model
parameter learning
AR( p ) model
small sample data missing
threat assessment and ranking
utility theory