基于时间序列预测的威胁估计方法

Threat assessment method based on time series forecast

针对在威胁估计的动态贝叶斯网络中,转移概率的获取和观测数据的缺失问题.建立时间序列预测模型,对缺失数据进行预测;在获得完整数据后,利用完整数据集和前向递归算法完成参数学习;通过动态贝叶斯网络对目标的威胁进行估计.仿真结果表明:相比于数学期望最大算法,时间序列方法预测数据精度较高,学习时间短,能大大提高来袭目标威胁估计的效率,满足实际作战需要.

Aiming at the problems of transition probability getting and observational data missing in dynamic Bayesian network of threat assessment,a time series forecasting model was set up.Then the complete data set and the forward recursive algorithm were applied to parameter learning after the full data got.The threat of target was assessed based on the dynamic Bayesian network.Simulation shows that：compared to the expectation-maximization algorithm,the time series method can get higher accuracy of forecast data, have shorter learning time,increase the efficiency of the threat assessment greatly,and meet the actual operational needs.