The aircraft wake vortex has important influence on the operation of the airspace utilization ratio. Particularly, the identification of\naircraft wake vortex using the pulsed Doppler lidar characteristics provides a new knowledge of wake turbulence separation\nstandards. This paper develops an efficient pattern recognition-based method for identifying the aircraft wake vortex measured\nwith the pulsed Doppler lidar. The proposed method is outlined in two stages. (i) First, a classification model based on support\nvector machine (SVM) is introduced to extract the radial velocity features in the wind fields by combining the environmental\nparameters. (ii) Then, grid search and cross-validation based on soft margin SVM with kernel tricks are employed to identify the\naircraft wake vortex, using the test dataset. The dataset includes wake vortices of various aircrafts collected at the Chengdu\nShuangliu International Airport from Aug 16, 2018, to Oct 10, 2018. The experimental results on dataset show that the proposed\nmethod can identify the aircraft wake vortex with only a small loss, which ensures the satisfactory robustness in\ndetection performance.
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