# Aircraft Wake Recognition Based on Improved ParNet Convolutional Neural Network
## 1 Introduction  
Aircraft wake refers to a pair of reverse spiral turbulent vortices formed behind aircraft wings owing to the pressure difference between the upper and lower surfaces of the wing during the flight. It accompanies the aircraft throughout the duration of the flight [1]. When an aircraft enters the wake area of another aircraft, its aerodynamic performance may be affected; this can significantly threaten its flight safety [2]. Wake is an important factor that affects the capacity and efficiency of airport runways with intensive take-off and landing. Therefore, the recognition of aircraft wake is of considerable significance in ensuring flight safety and improving airport operation efficiency. As regards numerical simulations, [PERSON] et al. analysed the radar characteristics of wake by employing a 3D large-eddy simulation [3]. They described the settings of the large-eddy simulation and explained the evolution of wake under three simulation conditions. [PERSON] et al. studied the impact of crosswind on wake attenuation and the acceleration mechanism of active flow control on wake attenuation [4]. The simulation results showed that the evident impact of the crosswind on the wake development is caused by the separation vortex generated near the crosswind field rather than the crosswind itself. [PERSON] et al. used an adaptive-grid large-eddy simulation technique to numerically study the near-ground evolution process of wake under crosswind conditions [5]. The results showed that the evolution of upstream and downstream vortices is asymmetric. The downstream vortex attenuates faster but moves farther. Moreover, the stronger the crosswind, the faster the wake vortex attenuates. [PERSON] et al. proposed a new probabilistic two-phase wake vortex decay model (P2P), which has become the most classic wake evolution model [6]. The model entails a set of functions based on aircraft and environmental parameters, which can predict the probability of wake behaviour in real-time. Light detection and ranging (lidar) is the most effective technical means to detect the near-ground wind field. In recent years, many researchers have studied wake phenomena by using the wind field data detected by wind lidar. [PERSON] et al. demonstrated the feasibility of using a 1.5 \(\upmu\)m pulse fibre lidar for wake detection at an airport [7]. [PERSON] et al. proposed an inversion method for an aircraft's wake vortex, which can estimate the horizontal and vertical positions of the wake and the mean circulation radius from the Doppler spectrum measured by the lidar [8]. [PERSON] et al. proposed that the aircraft wake can be identified by the main features of the wake Doppler spectrum [9]. However, this recognition method was limited by the sample size of the Doppler spectrum features, and the recognition process was redundant. [PERSON] et al. proposed a fast wake recognition method based on the CDL spectrum width and radial wind speed [10]. However, this method cannot adaptively adjust the recognition threshold of the Doppler spectrum features, and its recognition efficiency was limited. [PERSON] et al. proposed a recognition method based on the k-nearest neighbour (KNN) [11]; however, its recognition accuracy required improvement. [PERSON] et al. proposed a method for aircraft wake recognition based on the support vector machine (SVM) [12]. However, the single extraction method of the velocity's extremum features filtered the local information of the wake, limiting the recognition accuracy of the model on the measured data. In recent years, with the improvement and application of convolutional neural networks (CNNs) in computer vision, CNNs have increasingly been applied to studies of aircraft wake recognition. [PERSON] et al. classified the wake velocity nephogram based on AlexNet to achieve wake recognition, with its accuracy reaching 91.30% [13]. However, the AlexNet model required many parameters and calculations. A large amount of data was required to train the model, and it also consumed extensive computer resources.