Estimation of the wind velocity from the weak aerosol backscattering signals is an important problem in the research of Coherent Doppler lidar (CDL). The signal model and a real correlation random wind field algorithm are explained. The performance of the Maximum likelihood discrete spectral peak (ML DSP) algorithm for Coherent Doppler lidar is applied to simulation signal and summarized by employing the Monte Carlo simulations. The relationships between the SNR, estimation precision, and detection probability under different signal models are simulation and summarized. According to the analysis results of simulated, the ML DSP algorithm performance is the best for Hyperbolic-secant distribution, while Lorentz distribution has the worst performance. To satisfy the 80% detection probability, the turbulence model of Lorentz requires maximum signal-to-noise ratio (-16dB) in very strong turbulence intensity.
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