基于高斯拟合的相干激光雷达风速估计算法
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摘要
分别利用高斯拟合估计算法(Gaussian fitting estimation algorithm,以下简称Gauss估计算法)和最大似然(Maximum Likelihood, ML)离散谱峰值(Discrete Spectral Peak, DSP)估计算法(ML DSP)处理实测回波信号,计算得到风速扰动的功率谱密度(Power Spectral Density,PSD)。根据Kolmogorov湍流理论中PSD与频率的-5/3关系,比较不同距离门下的PSD,采用高频区域的风速误差作为风速估计性能参数,分析比较不同距离情况下风速误差,并利用自相关系数分析风速时间变化的相关性。结果表明:在距离较低的探测区域Gauss估计算法的风速误差微弱小于对应的ML DSP估计算法,二者之间的风速误差差值最多不超过0.05m/s。而在距离较高的区域,两种算法的风速误差差值从820m处的0.06 m/s增加至1 200 m的0.16 m/s。在风速的时间相关性分析上,Gauss估计算法的风速时间自相关系数明显大于对应的ML DSP估计算法,说明Gauss估计算法处理的风速数据更具有稳定性。
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 lowdetection 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 from0.06 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.
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 lowdetection 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 from0.06 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.
引文
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[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. 1.55μm all-fiber coherent Doppler lidar for wind measurement[J]. Infrared and Laser Engineering, 2016, 45(S1):S130001.(in Chinese)
[2] 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)
[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 1.55μm erbium fiber laser based lidar[C]//Proceedings of the13th 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. Longrange wind monitoring in real time with optimized coherent lidar[J]. Optical Engineering, 2017, 56(3):031217.
[13] Frehlich R. Simulation of coherent Doppler lidar performancein 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. 1.55μm all-fiber coherent Doppler lidar for wind measurement[J]. Infrared and Laser Engineering, 2016, 45(S1):S130001.(in Chinese)