为快速实现对战场态势的精确估计，提出了参数在线学习的动态贝叶斯网络方法：在基于专家知识确定的动态贝叶斯网络结构模型基础上，用前向递归方法对网络模型的参数进行估计 . 针对战场态势模型的观测值具有小样本的特性，以狄利克雷分布作为样本的先验分布，采用矩估计法对先验分布的超参数进行估计，以该先验分布的等价样本与观测值实现对网络参数的学习和对战场态势的估计 . 仿真实验结果表明，应用该方法实现态势估计具有较高的实时性和准确性 .

In order to assess battlefield situation accurately and quickly ， an algorithm for online parameters learning is proposed on the basis of dynamitic Bayesian networks （ DBN ） . Forward recursion algorithm is used to estimate the parameters of network model after the structure model of dynamic Bayesian network is confirmed by expert knowledge. Dirichlet distribution is used as the prior distribution of samples according to the characteristics of small samples for the observation value of battlefield situation model ， and moment estimation is adopt to estimate the hyper parameters of the prior distribution. Then ， in combination with the equivalent samples value from the prior distribution ， the observation value can be used to implement parameters learning and battlefield situation assessment. Simulated results indicate that the proposed algorithm is of good real-time performance and high accuracy for situation assessment.