In order to solve the flight safety issues threatened by wake vortex of leading aircraft, ensure air traffic safety, and improve the capacity of airdrome and airspace, an AlexNet convolutional neural network model algorithm is proposed to identify aircraft wake vortex. Combined with the detection principle of Doppler LiDAR and the classic model of Hallck-Burnham wake vortex velocity, the AlexNet neural network model was constructed to extract the image features of the wake vortex velocity images in the atmosphere and identify the aircraft wake vortex. The research shows that the model is able to accurately identify the aircraft wake vortex in the target airspace. After the network model converges, the accuracy rate reaches to 91.30%, which can effectively realize the identification work. Meanwhile, this study also demonstrates the low probability of false alarm of the AlexNet neural network in detecting wake vortex, which meets the requirement of early warning and monitoring of the aircraft wake vortex.
Research on aircraft wake vortex recognition using AlexNet
First published at:Jul 01, 2019
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Supported by National Natural Science Foundation of China (U1733203), Civil Aviation Authority Safety Capacity Building Project (TM2018-9-1/3), Sichuan Science and Technology Program (2018JY0394), and Innovation and Entrepreneurship Program of CAFUC (S201910624014)
Get Citation: Pan Weijun, Duan Yingjie, Zhang Qiang, et al. Research on aircraft wake vortex recognition using AlexNet[J]. Opto-Electronic Engineering, 2019, 46(7): 190082.