Method for generating simulation data of coherent wind lidar echo signal spectrum

By generating near-realistic coherent wind-measuring lidar echo signal spectrum simulation data, the problem of insufficient wind speed inversion accuracy under low signal-to-noise ratio conditions is solved, improving the efficiency and accuracy of neural network training, and applicable to various lidar models and wavelengths.

CN116256726BActive Publication Date: 2026-02-17UNIV OF SCI & TECH OF CHINA
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Patent Information

Application Number
CN202310474360.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-04-27
Publication Date
2026-02-17
Estimated Expiration
2043-04-27

AI Technical Summary

Technical Problem

Under low signal-to-noise ratio conditions, the accuracy of wind speed inversion by coherent wind lidar decreases. Traditional methods cannot effectively distinguish wind speeds at long distances, and the traditional lidar equations ignore the statistical characteristics of echo signals, resulting in insufficient simulation data to support neural network training.

Method used

By acquiring real echo signal spectrum data, removing background noise and calculating the variance of random noise, performing Gaussian fitting and normalization, recording the Doppler frequency shift difference, using a random number generator to generate bandwidth and power spectrum peak values ​​that satisfy a specific distribution, and combining simplified radar equations to generate simulated spectrum data.

Benefits of technology

It improves the realism of analog signals, reduces noise interference, enhances the efficiency and accuracy of neural network training, provides a scientific reference for wind speed inversion at long distances with low signal-to-noise ratios, and is applicable to coherent lidar of different models and wavelengths.

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Abstract

A coherent wind lidar echo signal spectrum simulation data generation method is provided, comprising: obtaining the bottom noise of the echo signal; removing the bottom noise from the echo signal spectrum data and counting the variance of the random noise; performing normalization processing on the random noise and performing Gaussian fitting to obtain the amplitude, Doppler shift and bandwidth of the echo signal; recording the Doppler shift difference between adjacent range gates to obtain the power spectrum peak energy of a single spectrum data and the correction amount of the distance; respectively counting the statistical parameters of the random variable, the power spectrum peak energy, the correction amount of the distance and the bandwidth; generating a group of wind speed profile graphs by using random walk and normal distribution characteristics of random variables, generating bandwidths under different range gates that satisfy the Lorentz distribution, power spectrum peak energies of a single simulated spectrum that satisfy the chi-square distribution, and distance correction amounts that satisfy the normal distribution; and generating a single simulated spectrum by a radar equation and repeatedly generating complete spectrum simulation data.
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