Mobile, all-fiber Doppler wind lidar based on a real-time motion wind retrieval algorithm method is demonstrated. The mobile wind lidar can negate the influences of attitude, velocity, and other factors on the Doppler frequency and is capable of obtaining more accurate real-time mid- to low-altitude wind field information over a large space. In the moving platform test, the measurement error of the wind speed is 0.33 m / s, and the measurement error of the wind direction is 5.32 deg.
In the past few decades, environmental pollution problems have become more and more serious because of development of modern industry and science technology. The real-time detection and identification of atmospheric pollutants, has aroused widespread concern. Differential Absorption Lidar (DIAL) technology has become a common method for atmospheric remote sensing pollution measurement. 9~11-m-band lasers have small scattering cross sections for the air pollution cloud particles, so the laser output energy is required to reach the Joule level. The system engineering is difficult to realize detection of air pollution clouds with technology. The developed remote sensing pollution detection system is used to detect the types, concentration, orientation and distance information of common industrial chemical pollution. It is mainly divided into two parts: DIAL channel and Front Range Detecting Lidar (FRDL) channel. This paper mainly discusses remote sensing of air pollution clouds using FRDL. The working principle of FRDL is to use the Doppler effect of aerosol on the laser backward Mie scattering to realize the measurement of the aerosol cloud. In July 2017, the verification test was completed at a certain place in the north of China. The simulated pollutant spraying device and sampler were set at 400m in front of the 5km target. The measured results are in agreement with the actual situation.
This paper uses the BP neural network and grey algorithm to forecast and study radar wind field. In order to reduce the residual error in the wind field prediction which uses BP neural network and grey algorithm, calculating the minimum value of residual error function, adopting the residuals of the gray algorithm trained by BP neural network, using the trained network model to forecast the residual sequence, using the predicted residual error sequence to modify the forecast sequence of the grey algorithm. The test data show that using the grey algorithm modified by BP neural network can effectively reduce the residual value and improve the prediction precision.