Volume 48 Issue 10
Oct.  2019
Turn off MathJax
Article Contents
Article Navigation >  Infrared and Laser Engineering  > 2019  >  48(10): 1005008-1005008(8)

Zhuang Zibo, Chen Xing, Tai Hongda, Song Delong, Xu Fengtian, Xing Zhiwei. Horizontal wind field estimation method based on dual Lidars[J]. Infrared and Laser Engineering, 2019, 48(10): 1005008-1005008(8). doi: 10.3788/IRLA201948.1005008
Citation: Zhuang Zibo, Chen Xing, Tai Hongda, Song Delong, Xu Fengtian, Xing Zhiwei. Horizontal wind field estimation method based on dual Lidars[J]. Infrared and Laser Engineering, 2019, 48(10): 1005008-1005008(8). doi: 10.3788/IRLA201948.1005008
shu

Horizontal wind field estimation method based on dual Lidars

doi: 10.3788/IRLA201948.1005008
  • 1.

    College of Flight Technology,Civil Aviation University of China,Tianjin 300300,China;

  • 2.

    Tianjin Key Laboratory of Air Traffic Management Operation Planning and Safety Technology,Civil Aviation University of China,Tianjin 300300,China;

  • 3.

    College of Electronic Information and Automation,Civil Aviation University of China,Tianjin 300300,China

  • Received Date: 2019-06-05
  • Rev Recd Date: 2019-07-15
  • Publish Date: 2019-10-25
  • Aiming at the problem of error in single Lidar detection of horizontal wind field in civil aviation airport area, a support vector regression based model for estimating horizontal wind field of double Lidars was proposed. The model was based on the wind speed of two Lidars overlapping scanning regions, and the horizontal wind speed at the intersection points was used to estimate the horizontal wind speed of other data points in the radial direction. Firstly, the three characteristics of radial wind speed, horizontal wind speed and distance in the overlapping area were extracted. The overlapping area data points were used as the training set. After the same dimension was normalized, the penalty factor and kernel function parameters were set, and the initial estimated value was obtained by support vector regression. Then, the radial wind speed of the single Lidar was used as the a priori condition to estimate the horizontal wind speed of the adjacent radial points in the non-overlapping area. Then, the radial wind speed of the single Lidar was used as the a priori condition to estimate the horizontal wind speed of the adjacent radial points in the non-overlapping area. The estimated results were extended to a new training set, and the training set was gradually expanded to estimate the horizontal wind speed in the non-overlapping area. Finally, the error of the stepwise estimation of the method was analyzed by the measured data. The influence of wind speed and echo signal-to-noise ratio on the estimation performance of the method was analyzed. The results show that the root mean square error of the wind field estimated by the method is better than that of the single radar. The method expands the range of the horizontal wind field detected by the dual Lidars and improves the utilization of the Lidars.
  • [1] Sathe A, Mann J. Measurement of turbulence spectra using scanning pulsed wind lidars[J]. Journal of Geophysical Research:Atmospheres, 2012, 117:D01201.
    [2] Lundquist J K, Churchfield M J, Lee S, et al. Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics[J]. Atmospheric Measurement Techniques, 2015, 8:907-920.
    [3] Zong Wenpeng, Li Guangyun, Li Minglei, et al. A survey of laser scan matching methods[J]. Chinese Optics, 2018,11(6):914-930. (in Chinese)宗文鹏, 李广云, 李明磊, 等. 激光扫描匹配方法研究综述[J]. 中国光学, 2018, 11(6):914-930.
    [4] Cheong B L, Yu T Y, Paler R D, et al. Effects of wind field inhomogeneities on Doppler beam swinging revealed by an imaging radar[J]. Atmos Ocean Tech, 2008, 25:1414-1422,.
    [5] Newman J F, Klein P M, Wharton S, et al. Evaluation of three lidar scanning strategies for turbulence measurements[J]. Atmospheric Measurement Techniques, 2016, 9(5):1993-2013.
    [6] Newman J F, Bonin T A, Klein P M, et al. Testing and validation of multi-lidar scanning strategies for wind energy applications[J]. Wind Energy, 2016, 19(12):2239-2254.
    [7] Chen Jianwu, Quan Sibo, Quan Yanming, et al. Calibration method of relative position and pose between dual two-dimensional laser radar[J]. Chinese Journal of Lasers, 2017, 44(10):1004005. (in Chinese)陈健武, 全思博, 全燕鸣, 等. 双二维激光雷达相对位姿的标定方法[J]. 中国激光, 2017, 44(10):1004005.
    [8] Wang Guocheng, Sun Dongsong, Duan Lianfei, et al. Analysis of factors affecting the data accuracy of Doppler wind lidar[J]. Acta Optica Sinica, 2015, 35(9):0901003. (in Chinese)王国成, 孙东松, 段连飞, 等. 多普勒测风激光雷达风场数据影响因素分析[J]. 光学学报, 2015, 35(9):0901003.
    [9] Feng Changzhong, Wu Songhua, Huang Haiguang, et al. Technique of wind field detection based on single Doppler lidar with gradient descent VAD method[J]. Infrared and Laser Engineering, 2018, 47(11):1106006. (in Chinese)冯长中, 吴松华, 黄海广, 等. 梯度下降VAD方法的单多普勒激光雷达风场探测技术[J]. 红外与激光工程, 2018, 47(11):1106006.
    [10] Chen Xing, Li Zhen, Zhuang Zibo, et al. A small scale wind shear detection algorithm of modified F-factor for wind-profiling lidar[J]. Optics and Precision Engineering, 2018, 26(4):927-935. (in Chinese)陈星, 李贞, 庄子波, 等. 测风激光雷达修正F因子的小尺度风切变检测算法[J]. 光学精密工程, 2018, 26(4):927-935.
    [11] Li Li, Wang Canzhao, Xie Yafeng, et al. Wind field inversion technique for scanning wind lidar[J]. Chinese Optics, 2013, 6(2):251-258. (in Chinese)李丽, 王灿召, 谢亚峰, 等. 扫描式测风激光雷达的风场反演[J]. 中国光学, 2013,6(2):251-258.
    [12] Liu Zhiqing, Li Pengcheng, Chen Xiaowei, et al. Classification of airborne LiDAR point cloud data based on information vector machiner[J]. Optics and Precision Engineering, 2016, 24(1):210-219. (in Chinese)刘志青, 李鹏程, 陈小卫, 等. 基于信息向量机的机载激光雷达点云数据分类[J]. 光学精密工程, 2016, 24(1):210-219.
    [13] Ding Shifei, Qi Bingjuan, Tan Hongyan. An overview on theory and algorithm of support vector machines[J]. Journal of University of Electronic Science and Technology of China, 2011, 40(1):2-10. (in Chinese)丁世飞, 齐丙娟, 谭红艳. 支持向量机理论与算法研究综述[J]. 电子科技大学学报, 2011, 40(1):2-10.
    [14] Sathe A, Mann J, Vasiljevic N, et al. A six-beam method to measure turbulence statistics using ground-based wind lidars[J]. Atmospheric Measurement Techniques, 2015, 8(2):729-740.
    [15] Zhou Yongsheng, Ma Xunpeng, Zhao Yiming, et al. Frequency estimation of the weak signal of the Coherent Wind Lidar[J]. Infrared and Laser Engineering, 2018, 47(3):0306002. (in Chinese)周永升, 马勋鹏, 赵一鸣, 等. 相干测风激光雷达微弱信号的频率估计[J]. 红外与激光工程, 2018, 47(3):0306002.
    [16] Zhuang Zibo, Liu Xiaoyu, Chen Xing. Estimation method of turbulent wind speed based on lidar pulse characteristics[J]. Infrared and Laser Engineering, 2018, 47(11):1106001. (in Chinese)庄子波, 刘晓宇, 陈星. 基于激光雷达脉冲特性的湍流风速估计方法[J]. 红外与激光工程, 2018, 47(11):1106001.
    [17] Frehlich R G, Yadlowsky M J. Performance of mean-frequency estimators for Doppler radar and lidar[J]. J Atoms Oceanic Technology, 1994, 11:1217-1230.
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
Get Citation
PDF
XML

Article Metrics

Article views(594) PDF downloads(54) Cited by()

Related
Proportional views

Horizontal wind field estimation method based on dual Lidars

doi: 10.3788/IRLA201948.1005008
  • 1. College of Flight Technology,Civil Aviation University of China,Tianjin 300300,China;
  • 2. Tianjin Key Laboratory of Air Traffic Management Operation Planning and Safety Technology,Civil Aviation University of China,Tianjin 300300,China;
  • 3. College of Electronic Information and Automation,Civil Aviation University of China,Tianjin 300300,China
  • Received Date: 2019-06-05
  • Rev Recd Date: 2019-07-15
  • Publish Date: 2019-10-25

Abstract: Aiming at the problem of error in single Lidar detection of horizontal wind field in civil aviation airport area, a support vector regression based model for estimating horizontal wind field of double Lidars was proposed. The model was based on the wind speed of two Lidars overlapping scanning regions, and the horizontal wind speed at the intersection points was used to estimate the horizontal wind speed of other data points in the radial direction. Firstly, the three characteristics of radial wind speed, horizontal wind speed and distance in the overlapping area were extracted. The overlapping area data points were used as the training set. After the same dimension was normalized, the penalty factor and kernel function parameters were set, and the initial estimated value was obtained by support vector regression. Then, the radial wind speed of the single Lidar was used as the a priori condition to estimate the horizontal wind speed of the adjacent radial points in the non-overlapping area. Then, the radial wind speed of the single Lidar was used as the a priori condition to estimate the horizontal wind speed of the adjacent radial points in the non-overlapping area. The estimated results were extended to a new training set, and the training set was gradually expanded to estimate the horizontal wind speed in the non-overlapping area. Finally, the error of the stepwise estimation of the method was analyzed by the measured data. The influence of wind speed and echo signal-to-noise ratio on the estimation performance of the method was analyzed. The results show that the root mean square error of the wind field estimated by the method is better than that of the single radar. The method expands the range of the horizontal wind field detected by the dual Lidars and improves the utilization of the Lidars.

    HTML

Reference (17)

Catalog

    版权所有 © 2019 《红外与激光工程》编辑部｜ Address: 58 Zhonghuan West Road, Tianjin Airport Economic Zone｜ Post Code: 300308

    津ICP备19007885号-1

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return