Near-Earth aircraft wake vortex recognition based on multiple LIDAR and transformer

Along with the rapid development of the air transportation industry, the impact of aircraft wake vortices on flight safety and airport capacity has become increasingly prominent. In this paper, we propose a transformer-based model to solve the problem of multiple LIDAR wake vortex detection and recognition in airports. By setting up multiple Doppler LIDARs in the near-Earth flight areas of different runways of Shenzhen Baoan Airport (SZX), a large amount of accurate wind field data is captured for wake vortex data collection. In the deep learning framework, the radial velocity sequence obtained from the LIDAR is used as the input of the transformer. Meanwhile, local meteorological information and LIDAR operating parameters are introduced into the model, providing prior knowledge at different observation points. The experimental results show that the model has unified modeling for different LIDAR wake vortex detection, and has obtained excellent recognition results.