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Algorithms of 3D Wind Field Reconstructing by Lidar Remote Sensing Data

机译:激光雷达遥感数据重建3D风场的算法

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In this paper, we analyzed the performance of wind vector field recovery from the wind lidar measurements. Wind lidar (LIDAR - Light Identification Detection And Ranging) remotely measures the wind radial speed by using the Doppler principle. Algorithms of the wind vector reconstruction using different versions of the least squares method are considered. In particular, the versions of weighted least squares (WLS) are considered, as well as the use of data spikes filtering procedures in the source data. The weights were calculated inversely with the local approximation error. As the initial data, the data of real measurements obtained in various wind conditions were used. The situations of a stationary wind field, a wind field with speed gusts, a wind field with fluctuations in direction, a wind field of variable speed and direction are considered. Lidar data were obtained for a region with a low-hilly terrain; therefore, even in the case of a stationary in time, the wind field was characterized by spatial heterogeneity. The questions of the use of regularization methods are considered. The analysis of the influence of the size of the averaging region on the quality of the recovery process was carried out.
机译:在本文中,我们从风激光雷达测量中分析了风矢量场恢复的性能。激光雷达(LIDAR-光识别检测和测距)使用多普勒原理远程测量风的径向速度。考虑了使用最小二乘法的不同版本重建风矢量的算法。尤其要考虑加权最小二乘(WLS)的版本,以及在源数据中使用数据尖峰滤波过程。权重与局部近似误差成反比。作为初始数据,使用在各种风况下获得的实际测量数据。考虑了固定风场,阵风风场,方向波动风场,速度和方向可变风场的情况。获得了低丘陵地区的激光雷达数据;因此,即使在时间静止的情况下,风场也具有空间异质性的特征。考虑使用正则化方法的问题。分析了平均区域的大小对恢复过程质量的影响。

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