尾流是飞机飞行时的必然产物，对航空安全有重大威胁，还限制着航空效率和容量的提升，飞机尾流涡核的精准辨识是动态缩减尾流间隔的前提，目前晴空尾涡探测的主要工具是相干多普勒激光雷达（CDL）。针对使用激光雷达进行飞机尾流探测中受限于雷达时空分辨率和背景风场的影响导致的识别和反演尾流关键参数误差较大这一问题，提出一种在激光雷达探测数据基础上基于贝叶斯网络（BN）和均方误差（MSE）构建的尾涡参数反演模型。搭建大气背景风和湍流环境，并将其叠加到模拟的尾涡速度场上，得到用于训练模型的仿真数据集。实验结果表明：所提算法能够得到误差较小的参数反演结果（仿真算例中涡核位置偏差在2 m以内，环量偏差在5%以内）；在实际算例中，所提算法与传统算法相比，反演速度场均方误差显著降低（平均超过50%）。本研究可用于机场实时尾涡监测，对尾涡间隔标准制定有重要意义。

Aircraft wake, an inevitable byproduct of aircraft flight, poses a major threat to aviation safety and limits the improvement of aviation efficiency and capacity. Accurate identification of aircraft wake vortex nuclei is a prerequisite for dynamically reducing wake intervals, and coherent Doppler Lidar (CDL) is the main tool for clear-air wake detection. To address the significant errors in identifying and inverting key parameters of aircraft wake turbulence caused by the limitations of CDL spatiotemporal resolution and background wind field effects, this study proposes a wake vortex parameter inversion model based on Bayesian network (BN) and mean squared error (MSE) using CDL detection data. An atmospheric background wind and turbulence environment are built and superimposed onto the simulated wake velocity field to obtain a simulation dataset for training the model. The results show that the proposed model can obtain parameter inversion results with small errors (within 2 meters deviation of the vortex core position and within 5% deviation of the ring volume in the simulated case) at an acceptable computational level. In actual cases, the mean squared error of the inversion velocity field is significantly reduced (more than 50% on average) compared with the conventional algorithm. This research can be used for real-time monitoring of wake vortices at airports and is of great significance for the development of wake interval standards.