Wind velocity estimation algorithm based on Gaussian fitting in coherent lidar

Wang Pingchun; Chen Tingdi; Zhou Anran; Han Fei; Wang Yuanzu; Sun Dongsong; Wang Guocheng

doi:10.3788/IRLA201847.1230006

Volume 47 Issue 12

Jan. 2019

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Wang Pingchun, Chen Tingdi, Zhou Anran, Han Fei, Wang Yuanzu, Sun Dongsong, Wang Guocheng. Wind velocity estimation algorithm based on Gaussian fitting in coherent lidar[J]. Infrared and Laser Engineering, 2018, 47(12): 1230006-1230006(6). doi: 10.3788/IRLA201847.1230006

Citation:

Wang Pingchun, Chen Tingdi, Zhou Anran, Han Fei, Wang Yuanzu, Sun Dongsong, Wang Guocheng. Wind velocity estimation algorithm based on Gaussian fitting in coherent lidar[J]. Infrared and Laser Engineering, 2018, 47(12): 1230006-1230006(6). doi: 10.3788/IRLA201847.1230006

Wind velocity estimation algorithm based on Gaussian fitting in coherent lidar

doi: 10.3788/IRLA201847.1230006

1.
School of Earth and Space Science,University of Science and Technology of China,Hefei 230026,China;
2.
New Star Institute of Applied Technology,Hefei 230031,China

Received Date: 2018-07-05
Rev Recd Date: 2018-08-15
Publish Date: 2018-12-25

Abstract

The power spectral density(PSD) of the wind velocity disturbance was calculated by processing the measured echo signals by using Gaussian fitting estimation algorithm and maximum likelihood (ML)discrete spectral peak(DSP) estimation algorithm respectively. According to Kolmogorov turbulence theory, PSD has the relationship of -5/3 slope of frequency. It could be compared by different PSD under different distance gates. Wind velocity error in the high frequency region was used as the parameter of wind velocity estimation for comparing performance, and the error under different distances was analyzed and compared. The correlation of the relationship between wind velocity and time series was analyzed by using the autocorrelation coefficient. The results show that the wind velocity error of Gaussian fitting estimation algorithm is less than that of the corresponding ML DSP estimation algorithm in the low detection area, and the difference between the two wind speed errors does not exceed 0.05 m/s. In the area with higher distance, the difference of wind velocity error between the two algorithms increases from 0.06 m/s at 820 m to 0.16 m/s at 1 200 m. In the time-dependent analysis of the wind velocity, the autocorrelation coefficient of Gaussian fitting estimation algorithm between wind velocity and time is significantly larger than that of the corresponding ML DSP estimation algorithm, which shows that the wind velocity data processed by Gaussian fitting estimation algorithm is more stable.
- coherent lidar,
- wind velocity estimation,
- maximum likelihood estimator,
- Gaussian fitting

References

[1]	Wang Guining, Liu Bingyi, Feng Changzhong, et al. Data quality control method for VAD wind field retrieval based on coherent wind lidar[J]. Infrared and Laser Engineering, 2018, 47(2):0230002. (in Chinese)
[2]	Liu Qiuwu, Chen Yafeng, Wang Jie, et al. Effects of wavelength shift and energy fluctuation on inversion of NO2 differential absorption lidar[J]. Optics and Precision Engineering, 2018, 26(2):253-260. (in Chinese)
[3]	Lu Xianyang, Li Xuebin, Qin Wubin, et al. Retrieval of horizontal distribution of aerosol mass concentration by micro pulse lidar[J]. Optics and Precision Engineering, 2017, 25(7):1697. (in Chinese)
[4]	Rye B J, Hardesty R. Discrete spectral peak estimation in incoherent backscatter heterodyne lidar. I. Spectral accumulation and the Cramer-Rao lower bound[J]. IEEE Transactions on Geoscience and Remote Sensing, 1993, 31(1):16-27.
[5]	Frehlich R, Yadlowsky M. Performance of mean-frequency estimators for Doppler radar and lidar[J]. Journal of Atmospheric and Oceanic Technology, 1994. 11(5):1217-1230.
[6]	Rye B J. Estimate optimization parameters for incoherent backscatter heterodyne lidar including unknown return signal bandwidth[J]. Applied Optics, 2000. 39(33):6086-6096.
[7]	Frehlich R. Scanning Doppler lidar for input into short-term wind power forecasts[J]. Journal of Atmospheric and Oceanic Technology, 2013, 30(2):230-244.
[8]	Valla M. Fourier transform maximum likelihood estimator for distance resolved velocity measurement with a pulsed 1.55m erbium fiber laser based lidar[C]//Proceedings of the 13th Coherent Laser Radar Conference, 2005.
[9]	Dabas A M. Semiempirical model for the reliability of a matched filter frequency estimator for Doppler lidar[J]. Journal of Atmospheric and Oceanic Technology, 1999, 16(1):19-28.
[10]	Dabas A M, Philippe D,Pierre H F, et al. Adaptive filters for frequency estimate of heterodyne Doppler lidar returns:Recursive implementation and quality control[J]. Journal of Atmospheric and Oceanic Technology, 1999, 16(3):361-372.
[11]	Dabas A M, Philippe D,Pierre H F, et al. Velocity biases of adaptive filter estimates in heterodyne Doppler lidar measurements[J]. Journal of Atmospheric and Oceanic Technology, 2000, 17(9):1189-1202.
[12]	Dolfi-Bouteyre A, Guillaume C, Laurent L, et al. Long-range wind monitoring in real time with optimized coherent lidar[J]. Optical Engineering, 2017, 56(3):031217.
[13]	Frehlich R. Simulation of coherent Doppler lidar performance in the weak-signal regime[J]. Journal of Atmospheric and Oceanic Technology, 1996, 13(3):646-658.
[14]	Frehlich R, Stephen M H, Sammy W H, et al. Coherent Doppler lidar measurements of winds in the weak signal regime[J]. Applied Optics, 1997, 36(15):3491-3499.
[15]	Frehlich R, Stephen M H, Sammy W H, et al. Coherent Doppler lidar measurements of wind field statistics[J]. Boundary-Layer Meteorology, 1998, 86(2):233-256.
[16]	Zhou Yinjie, Zhou Anran, Sun Dongsong, et al. Development of differential image motion LiDAR for profiling optical turbulence[J]. Infrared and Laser Engineering, 2016. 45(11):1130001. (in Chinese)
[17]	Feng Shuanglian, QiangXiwen, Zong Fei, et al. Data processing techniques for turbulence profile Lidar[J]. Infrared and Laser Engineering, 2015, 44(S1):220-224.(in Chinese)
[18]	Frehlich R. Estimation of velocity error for Doppler lidar measurements[J]. Journal of Atmospheric and Oceanic Technology, 2001, 18(10):1628-1639.
[19]	Hu Yang, Zhu Heyuan. 1.55m all-fiber coherent Doppler lidar for wind measurement[J]. Infrared and Laser Engineering, 2016, 45(S1):S130001. (in Chinese)

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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

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Wind velocity estimation algorithm based on Gaussian fitting in coherent lidar

1. School of Earth and Space Science,University of Science and Technology of China,Hefei 230026,China;

2. New Star Institute of Applied Technology,Hefei 230031,China

Received Date: 2018-07-05

Rev Recd Date: 2018-08-15

Publish Date: 2018-12-25

Key words:

Abstract: The power spectral density(PSD) of the wind velocity disturbance was calculated by processing the measured echo signals by using Gaussian fitting estimation algorithm and maximum likelihood (ML)discrete spectral peak(DSP) estimation algorithm respectively. According to Kolmogorov turbulence theory, PSD has the relationship of -5/3 slope of frequency. It could be compared by different PSD under different distance gates. Wind velocity error in the high frequency region was used as the parameter of wind velocity estimation for comparing performance, and the error under different distances was analyzed and compared. The correlation of the relationship between wind velocity and time series was analyzed by using the autocorrelation coefficient. The results show that the wind velocity error of Gaussian fitting estimation algorithm is less than that of the corresponding ML DSP estimation algorithm in the low detection area, and the difference between the two wind speed errors does not exceed 0.05 m/s. In the area with higher distance, the difference of wind velocity error between the two algorithms increases from 0.06 m/s at 820 m to 0.16 m/s at 1 200 m. In the time-dependent analysis of the wind velocity, the autocorrelation coefficient of Gaussian fitting estimation algorithm between wind velocity and time is significantly larger than that of the corresponding ML DSP estimation algorithm, which shows that the wind velocity data processed by Gaussian fitting estimation algorithm is more stable.

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Reference (19)

[1]	Wang Guining, Liu Bingyi, Feng Changzhong, et al. Data quality control method for VAD wind field retrieval based on coherent wind lidar[J]. Infrared and Laser Engineering, 2018, 47(2):0230002. (in Chinese)
[2]	Liu Qiuwu, Chen Yafeng, Wang Jie, et al. Effects of wavelength shift and energy fluctuation on inversion of NO2 differential absorption lidar[J]. Optics and Precision Engineering, 2018, 26(2):253-260. (in Chinese)
[3]	Lu Xianyang, Li Xuebin, Qin Wubin, et al. Retrieval of horizontal distribution of aerosol mass concentration by micro pulse lidar[J]. Optics and Precision Engineering, 2017, 25(7):1697. (in Chinese)
[4]	Rye B J, Hardesty R. Discrete spectral peak estimation in incoherent backscatter heterodyne lidar. I. Spectral accumulation and the Cramer-Rao lower bound[J]. IEEE Transactions on Geoscience and Remote Sensing, 1993, 31(1):16-27.
[5]	Frehlich R, Yadlowsky M. Performance of mean-frequency estimators for Doppler radar and lidar[J]. Journal of Atmospheric and Oceanic Technology, 1994. 11(5):1217-1230.
[6]	Rye B J. Estimate optimization parameters for incoherent backscatter heterodyne lidar including unknown return signal bandwidth[J]. Applied Optics, 2000. 39(33):6086-6096.
[7]	Frehlich R. Scanning Doppler lidar for input into short-term wind power forecasts[J]. Journal of Atmospheric and Oceanic Technology, 2013, 30(2):230-244.
[8]	Valla M. Fourier transform maximum likelihood estimator for distance resolved velocity measurement with a pulsed 1.55m erbium fiber laser based lidar[C]//Proceedings of the 13th Coherent Laser Radar Conference, 2005.
[9]	Dabas A M. Semiempirical model for the reliability of a matched filter frequency estimator for Doppler lidar[J]. Journal of Atmospheric and Oceanic Technology, 1999, 16(1):19-28.
[10]	Dabas A M, Philippe D,Pierre H F, et al. Adaptive filters for frequency estimate of heterodyne Doppler lidar returns:Recursive implementation and quality control[J]. Journal of Atmospheric and Oceanic Technology, 1999, 16(3):361-372.
[11]	Dabas A M, Philippe D,Pierre H F, et al. Velocity biases of adaptive filter estimates in heterodyne Doppler lidar measurements[J]. Journal of Atmospheric and Oceanic Technology, 2000, 17(9):1189-1202.
[12]	Dolfi-Bouteyre A, Guillaume C, Laurent L, et al. Long-range wind monitoring in real time with optimized coherent lidar[J]. Optical Engineering, 2017, 56(3):031217.
[13]	Frehlich R. Simulation of coherent Doppler lidar performance in the weak-signal regime[J]. Journal of Atmospheric and Oceanic Technology, 1996, 13(3):646-658.
[14]	Frehlich R, Stephen M H, Sammy W H, et al. Coherent Doppler lidar measurements of winds in the weak signal regime[J]. Applied Optics, 1997, 36(15):3491-3499.
[15]	Frehlich R, Stephen M H, Sammy W H, et al. Coherent Doppler lidar measurements of wind field statistics[J]. Boundary-Layer Meteorology, 1998, 86(2):233-256.
[16]	Zhou Yinjie, Zhou Anran, Sun Dongsong, et al. Development of differential image motion LiDAR for profiling optical turbulence[J]. Infrared and Laser Engineering, 2016. 45(11):1130001. (in Chinese)
[17]	Feng Shuanglian, QiangXiwen, Zong Fei, et al. Data processing techniques for turbulence profile Lidar[J]. Infrared and Laser Engineering, 2015, 44(S1):220-224.(in Chinese)
[18]	Frehlich R. Estimation of velocity error for Doppler lidar measurements[J]. Journal of Atmospheric and Oceanic Technology, 2001, 18(10):1628-1639.
[19]	Hu Yang, Zhu Heyuan. 1.55m all-fiber coherent Doppler lidar for wind measurement[J]. Infrared and Laser Engineering, 2016, 45(S1):S130001. (in Chinese)

Wind velocity estimation algorithm based on Gaussian fitting in coherent lidar

doi: 10.3788/IRLA201847.1230006

Abstract

References

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通讯作者: 陈斌, bchen63@163.com

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