Clear-air turbulence recognition by Doppler-wind-lidar in terminal area based on DCGAN

A study was conducted on the problem of insufficient turbulence samples and low recognition rate when using Doppler wind lidar for clear-air turbulence recognition in the flight terminal area. An improved Deep Convolutional Generative Adversarial Network (DCGAN) algorithm was proposed. Eddy Dissipation Rate (EDR) images were constructed using the radial wind speed data of nine months from the Doppler wind lidar experimental platform of the Lanzhou Zhongchuan International Airport. Samples with clear-air turbulence were selected to construct a turbulence sample set, the DCGAN structure was improved by expanding the convolutional layer and transposing the convolutional layer, so as to achieve sample expansion. The post-confrontation was then used for recognition. The results show that the recognition accuracy of the post-confrontation discriminator trained with the original sample set and that trained with the augmented sample set are both better than that trained with the Convolutional Neural Network (CNN) and the pre-confrontation discriminator, with improvements of 6.55%, 8.25%, and 0.31%, 1.9%, respectively. A comparison with the measured samples shows that the recognition accuracy was improved by 3.33% and 6.67%, verifying the feasibility of the proposed method.