基于奇异值分解的激光雷达湍流预警算法

提出了一种基于奇异值分解(Singular Value Decomposition,SVD)的湍流速度结构函数构造方法,将该方法构造的速度结构函数与湍流模型拟合,可以实现激光雷达的湍流识别。首先对激光雷达扫描的空间数据进行距离门扇区划分,在每个子扇区内对湍流风场做奇异值分解,得到特征速度基准值和每个距离门的湍流脉动速度,构建出速度结构函数。选取标准von Kármán湍流模型函数作为拟合约束,得出涡流耗散率的立方根来判断湍流的强度。最后,利用兰州机场的实测数据,对比分析了在不同湍流强度下SVD方法的速度结构函数与局部平均方法的性能。通过与机组报告的湍流数据进行对比分析,SVD方法进行湍流预警的预警率可以达到85.2%。该方法对提高机场湍流探测和识别有重要意义。

A Turbulence Alerting Algorithm Based on Lidar's Singular Value Decomposition

A singular value decomposition (SVD) based turbulence velocity structure function construction method is proposed. The velocity structure function constructed by the method is fitted with the turbulence model to realize the turbulence identification of the laser radar. Firstly, the spatial data of the lidar scanning is divided into the distance gate sectors. The singular value decomposition is performed on the turbulent wind field in each sub-sector, and the characteristic velocity reference value and the turbulent pulsation velocity of each distance gate are obtained to construct the velocity structure function. The standard von Kármán turbulence model function is selected as the fitting constraint, and the cube root of the eddy current dissipation rate is obtained to judge the intensity of the turbulence. Finally, using the measured data of Lanzhou Airport, the performance of the velocity structure function and the local average method of the SVD method under different turbulence intensities are compared and analyzed. The turbulence data reported by the crew is compared and analyzed, and the SVD method is used to predict the turbulence warning. Can reach 85.2%. This method is of great significance for improving airport turbulence detection and identification.