A Probability-Constraint-Based Method for Robust Wind Velocity Estimation in Lidar Doppler Spectrograms With Low Signal-to-Noise Ratio

It is quite challenging for conventional estimators to extract the radial wind velocities from the raw spectrum data of coherent Doppler wind Lidar (CDWL) at very low signal-to-noise ratios (SNR). This work proposes a new wind velocity estimation method based on the constraints from neighboring wind profiles, which can effectively and robustly extend the reliable detection range of CDWL. Considering the spatial continuity of the wind field, priori information from the velocity probability distributions in previous range bins is utilized to reshape the contaminated spectrum of the current range bin. The influences of different preceding range bins are weighted by their overlapping ratios to the contaminated range bin. Iterations are carried out when computing the variance of the Gaussian-shaped probability, which can preserve the wind field details while removing the parameter dependence in practical applications. The method is verified theoretically in simulated atmosphere echoes as well as experimentally in our self-developed dual-frequency pulsed CDWL system. The first results show that reasonable estimates can be obtained in places far beyond the previous detection boundary provided by conventional estimators.