Aircraft Wake Recognition Based on Improved ParNet Convolutional Neural Network

The occurrence of wake can pose a threat to the flight safety of aircraft and affect the runway capacity and airport operation efficiency. To effectively identify aircraft wake, this paper proposes a novel convolutional neural network (CNN) method of aircraft wake recognition based on the improved parallel network (ParNet). Depthwise separable convolution (DSC) was introduced into the ParNet to make the wake recognition model lightweight. In addition, the convolutional block attention module (CBAM) was introduced into the wake recognition model to improve the capacity of the model to extract the spatial features of the wind field. The proposed aircraft wake recognition method was used to identify the aircraft wake based on the lidar wind field scanning image of Hong Kong International Airport. The best wake recognition effect was obtained with a recognition accuracy of 98.91% and an F1 value of 98.90%. As the number of parameters of the model was only 0.46 M, the aircraft wake could be identified on an ordinary computer. Thus, the proposed method can effectively identify aircraft wake.