Objective In the data processing of wind field detection by coherent Doppler lidar, the Doppler frequency shift is extracted as the target for wind speed calculation, and the accuracy of Doppler frequency estimation directly affects the performance of wind field detection by coherent Doppler lidar. The accuracy of wind measurement is greatly affected by the interference of noise clutter, weakness of reflection signal, and wind field inhomogeneity, thus limiting the detection performance of the system, resulting in wind speed estimation outliers and detection range faults. The existing research on the power spectrum analysis method lacks the targeted research and multi-angle optimization attempts under the key technical limitations of weak signals. Therefore, effective peak retrieval of the power spectrum plays a decisive role in achieving accurate inversion of the wind field under the application limitation. Therefore, a power spectrum analysis method is proposed to improve the accuracy and detection performance of coherent Doppler lidar wind speed retrieval.
Methods In order to improve the peak detection accuracy of the target signal under weak signal conditions and obtain the accurate frequency estimation of signal spectrum for wind speed inversion, the optimization of the frequency shift estimation algorithm and peak detection are explored. Specific optimization measures include the smoothing processing of the original power spectrum baseline: background noise removal algorithm (Fig.1-2), the resolution enhancement peak detection algorithm for the target signal (Fig.3), and the quality assessment of peak retrieval to achieve frequency estimation correction (Fig.4-5). A power spectrum analysis method based on nonlinear least squares noise fitting, combining linear prediction spectrum estimation and derivative enhancement algorithm is proposed (Fig.6).
Results and Discussions The commonly used maximum likelihood discrete spectrum peak estimation algorithm based on Fast Fourier Transform and the proposed frequency estimation synthesis algorithm are respectively applied to the measured radial wind speed data of coherent Doppler lidar, and the performance of the frequency estimation synthesis algorithm is evaluated. After applying the proposed algorithm, the stability of wind speed measurement has been significantly improved, the wind speed measurement error on several far-field distance bins has been effectively reduced, and the effective detection distance of wind speed has been effectively improved in all scanning directions. Through the statistical analysis of the autocorrelation coefficient, the temporal correlation and spatial correlation of the wind speed estimation are verified (Fig.10-11). The results show that the wind speed data obtained by the proposed power spectrum analysis method maintains good spatio-temporal continuity and spatial autocorrelation characteristics. The inversion results of lidar were compared with the reference results of the ultrasonic anemometer under spatio-temporal matching (Fig.15-16), and the effectiveness of the proposed power spectrum analysis method for improving the detection performance of coherent Doppler lidar was verified.
Conclusions On the basis of Fast Fourier Transform, a power spectrum analysis method based on nonlinear least squares noise fitting, combining linear prediction spectrum estimation and derivative enhancement algorithm is proposed. The algorithm has the characteristics of high noise suppressing effect, great recognition ability of weak signal, and high accuracy of wind speed estimation. The results of temporal and spatial correlation analysis of wind speed data show that the proposed power spectrum analysis method has better stability of wind speed estimation, and the effective wind field detection distance is increased by 73.13% compared with the ML DSP algorithm. The comparison results with the ultrasonic anemometer show that the proposed algorithm has high recognition accuracy in the case of weak signal. The standard deviation between the wind speed results and the ultrasonic anemometer is reduced by 0.23 m/s compared with the ML DSP algorithm, and the BIAS rate is reduced by 0.3 m/s, effectively improving the accuracy of Doppler frequency estimation in the low SNR range.
DownLoad: