针对现代复杂战场环境下威胁等级评估信息的不确定性，结合模糊数学及贝叶斯网提出了基于模糊贝叶斯网的威胁等级评估方法。在充分考虑威胁源相对于UCAV的距离、方位角对其隐身能力影响的基础上，从不确定性知识的概率化入手，综合天气、威胁类型、距离、方位角等不确定因素对威胁等级进行评估，采用加拿大Norsys软件公司的Netica软件建立贝叶斯网威胁评估模型并进行仿真。结果表明，该方法能快速、准确地评估威胁等级，具有一定的参考价值。

Aiming at the uncertainty of Threat Level Assessment (TLA) data sources under modern complex battlefielda fuzzy Bayesian network TLA method was proposed by integrating the fuzzy set theory into Bayesian networks.After well considering about the effect of the distance and azimuth angle of threat sources relative to a UCAV on its stealth capabilitythe threat level was evaluated by integrating such uncertain factors as weatherthreat typedistance and azimuth angle based on randomization of uncercain knowledge.Then a TLA Bayesian network was established by adopting Netica software of the Norsys Software Company in Canadaand simulation was carried out.The simulation results show that the method can assess the threat level rapidly and accurately.