Deep Learning for Aircraft Wake Vortex Identification

Abstract

Aircraft wake vortex refers to a pair of closed vortices due to the air pressure difference between the upper and lower surfaces of the wing when the aircraft is flying. During the take-off and landing of the aircraft, the wake vortex of the preceding aircraft threatened the safety of the aircraft following its approach. Especially in the near-field phase, due to the low altitude of the aircraft, the wake vortex is more harmful under the influence of the ground effect. Therefore, the wake vortex of the front aircraft can not be ignored. In order to maintain air traffic safety and accelerate the smooth flow of air traffic, this paper used a YOLO v3 model based on the principle of deep learning to identify the wake vortex generated by the aircraft. Through laser radar scanning, data processing, and deep neural network simulation experiments, the results show that the model can achieve high confidence recognition of the aircraft wake vortex, and can provide air traffic controllers with auxiliary decision information in actual work to ensure aircraft safety.