Wind velocity estimation algorithm based on Gaussian fitting in coherent lidar

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.