Performance of estimated Doppler velocity by maximum likelihood based on covariance matrix

Yanwei Wu; Pan Guo; Siying Chen; Yinchao Zhang; He Chen

doi:10.1117/1.OE.55.9.096112

29 September 2016 Performance of estimated Doppler velocity by maximum likelihood based on covariance matrix

Yanwei Wu, Pan Guo, Siying Chen, Yinchao Zhang, He Chen

Author Affiliations +

Optical Engineering, Vol. 55, Issue 9, 096112 (September 2016). https://doi.org/10.1117/1.OE.55.9.096112

Abstract

This paper investigates the efficient estimator of echo data processing to clean the spectrum through the denoising process. The maximum likelihood based on covariance matrix (MLCM) method without a priori knowledge of the spectral width is proposed for denoising the atmospheric signal. This method is applied to simulated and actual data to estimate the spectrum parameters. The probability density function of estimators as an empirical model is used to describe the performance of the estimators. The MLCM method is suggested to be an alternate estimator to precisely obtain the essential spectrum parameters with a lower standard deviation of good estimators and a larger detected range, which is improved by 20%, compared with the maximum likelihood method with a priori knowledge of the spectral width. Moreover, it can reduce the large velocity volatility and the uncertainties of the spectral width in the low signal-to-noise ratio regime. The MLCM method can be applied to obtain the whole wind profiling by the coherent Doppler lidar.

Citation Download Citation

Yanwei Wu, Pan Guo, Siying Chen, Yinchao Zhang, and He Chen "Performance of estimated Doppler velocity by maximum likelihood based on covariance matrix," Optical Engineering 55(9), 096112 (29 September 2016). https://doi.org/10.1117/1.OE.55.9.096112

Published: 29 September 2016

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