基于奇异值分解的激光雷达湍流预警算法

庄子波; 陈星; 台宏达; 宋德龙; 陈柏纬

doi:10.3788/OPE.20192703.0671

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基于奇异值分解的激光雷达湍流预警算法

信息科学 | 更新时间：2020-08-13

- 基于奇异值分解的激光雷达湍流预警算法
- Turbulence alerting algorithm based on singular value decomposition of Lidar
- 光学精密工程 2019年27卷第3期页码：671-679
- 作者机构：
  
  1.中国民航大学飞行技术学院，天津 300300
  2.中国民航大学天津市空管运行规划与安全技术重点实验室, 天津 300300
  3.中国民航大学电子信息与自动化学院, 天津 300300
  4.香港天文台，香港 999077
- 作者简介：
  
  [ "庄子波(1980-)，男，山东诸城人，硕士，副教授，2002年于山东师范大学获得学士学位，2007年于中国民航大学获得硕士学位，主要研究方向为航空气象、激光雷达技术及空间风场探测风切变识别等研究工作。E-mail:zhuangzibo@126.com" ]
  [ "陈星(1984-)，男，河北辛集人，硕士，研究实习员，2008年于天津商业大学获得学士学位，2014年于中国民航大学获得硕士学位，主要研究方向为激光雷达技术及风切变识别等研究工作。E-mail: xchencauc@163.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(U1433202);中央高校基金资助项目(3122018F008)
- DOI：10.3788/OPE.20192703.0671
  中图分类号： P412.1
- 收稿日期：2018-10-09，
  
  录用日期：2018-11-2，
  
  纸质出版日期：2019-03-15
- 稿件说明：
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庄子波, 陈星, 台宏达, 等. 基于奇异值分解的激光雷达湍流预警算法[J]. 光学精密工程, 2019,27(3):671-679.

Zi-bo ZHUANG, Xing CHEN, Hong-da TAI, et al. Turbulence alerting algorithm based on singular value decomposition of Lidar[J]. Optics and precision engineering, 2019, 27(3): 671-679.
庄子波, 陈星, 台宏达, 等. 基于奇异值分解的激光雷达湍流预警算法[J]. 光学精密工程, 2019,27(3):671-679. DOI： 10.3788/OPE.20192703.0671.

Zi-bo ZHUANG, Xing CHEN, Hong-da TAI, et al. Turbulence alerting algorithm based on singular value decomposition of Lidar[J]. Optics and precision engineering, 2019, 27(3): 671-679. DOI： 10.3788/OPE.20192703.0671.

摘要

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。

Abstract

A Singular Value Decomposition (SVD) based turbulence velocity structure function construction method was proposed. The velocity structure function constructed by the method was fitted with a turbulence model to realize the turbulence identification of a laser radar. First

the spatial data of lidar scanning was divided into distance gate sectors. Singular value decomposition was then performed on the turbulent wind field in each subsector

and the characteristic velocity reference value and turbulent pulsation velocity of each distance gate were obtained to construct the velocity structure function. The standard von Kármán turbulence model function was selected as the fitting constraint

and the cube root of the eddy current dissipation rate was obtained to assess the intensity of the turbulence. Finally

through measured data obtained from Lanzhou Airport

the performance of the velocity structure function and local average method of the SVD method under different turbulence intensities were compared and analyzed. The turbulence data reported by the crew were compared and analyzed

and the SVD method was used to predict the turbulence warning

which could reach 85.2%. This method is of great significance for improving airport turbulence detection and identification.

关键词

Keywords

references

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