We present the performance of the portable wind light detection and ranging (LIDAR) system based on the 1.55-μm all-fiber technology in the atmospheric boundary layer. The LIDAR is 23.9 kg in weight, 50 cm in height, 35 cm in width, and 27 cm in depth, and the system’s local oscillator (LO) light power, pulse energy, and pulse width are adjustable. The LO light power is optimized to 3 mW, to minimize the effect of the relative intensity noise. The transmitting pulse energy is reduced to 19 μJ, to minimize the system’s power consumption while covering a detection height of >1 km in clear-sky condition. The pulse width is variable from 100 to 400 ns corresponding to a minimum resolution from 15 to 60 m. The signal-to-noise ratio performance experiment shows that this system can detect as high as 2.1 km. Field experiments compared with radiosonde and anemometer show that this system presents a detection accuracy of better than 1 m / s and 10 deg.
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.
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