将动态贝叶斯网络应用于机载雷达侦察效能评估中,从而实现了一段时间内机载雷达侦察效能的动态评估。选取随战场环境、操作员水平等变化的因素作为影响机载雷达侦察效能评估的指标,并采用模糊分类方法形成各指标的状态集合;构建基于Netica 的动态贝叶斯网络模型,运用德尔菲法确定模型参数及隶属度函数,利用贝叶斯网络的推理模型以及相应的推理算法,对机载雷达侦察效能进行动态评估。仿真结果表明:该方法能够科学、有效地评估机载雷达侦察效能。

The dynamic Bayesian network is applied to airborne radar reconnaissance effectiveness evaluation, which can dynamic evaluate the effectiveness of airborne radar reconnaissance in a period of time. The changes factors such as the battlefield environment and operator level is selected as the index that can influence effectiveness evaluation of airborne radar reconnaissance, and the fuzzy classification method is used to form the index of the state. Dynamic Bayesian network model of airborne radar reconnaissance effectiveness evaluation based on Netica is established by reasoning model and algorithm of Bayesian network, and model parameter is confirmed by Delphic. The conclusion was shown by simulation results that the method can scientifically and effectively evaluate the airborne radar reconnaissance effectiveness.

TN959.1