## 1 Introduction  
In recent years, incidents of drones invading the airspace around airports and interfering with civil manned aircraft have occurred frequently, resulting in the inbound flights of airports being diverted to other places and large-scale delays of outbound flights. Since most of the drones intruding at airports are non-cooperative drones, the flight information between the drone and the manned aircraft cannot be exchanged in real-time. When a drone is detected, it is difficult to determine the position of the drone at the next moment accurately and efficiently, as their intentions are always unknown. The conflict risk between the drone and the manned aircraft is uncertain and the risk severity is unclear. The risk assessment of conflict between drones and manned aircraft is an important means of airport safety management. The collision probability prediction and risk assessment between drones and manned aircraft in low-altitude environments can provide technical support for the early warning of conflict in the context of air traffic. Until recently, a series of studies have been conducted concerning aircraft trajectory prediction. During the take-off, landing, and cruise phases, the manned aircraft basically follows the flight path, as they take off and land according to arrival and departure procedures. However, there is often a large temporal or spatial deviation for the specific track points as compared with the scheduled path, especially in complex air traffic environments [1]. In order to improve the safety of air traffic, it is necessary to predict the future trajectory points according to the historical trajectory of manned aircraft and drones, so as to assist the collision probability analysis between them. The trajectory prediction models of traditional civil aircraft can be divided into prediction based on state estimation, prediction based on dynamics, and prediction based on machine learning [2]. Common predictions based on state estimation include the Kalman filter model [3], the particle filter algorithm [4], the hidden Markov model [5], etc. For example, the hidden Markov model is applied together with a Gaussian mixture model [6] or trajectory similarity [7] to represent the position and altitude transition patterns of the aircraft during flight operation. Since these models are only based on the state estimation of kinematic equations, they are only suitable for short-term prediction, and the prediction error is often large. Prediction based on dynamics relies on a large number of performance parameters under some ideal assumptions [8]. [PERSON] et al. presented a set of methods for extracting different aircraft performance parameters for common aircraft types. The parametric models combined can be used to describe a complete flight that includes takeoff, initial climb, climb, cruise, descent, final approach, and landing [9]. However, the parameters vary significantly across different types of aircraft, i.e., the civil aircraft [10] and the general aviation aircraft [11]. The high dimensionality of the problem and nonlinearities in aircraft dynamics and control limits the use of common dynamic methods to obtain accurate prediction results. On the other hand, prediction based on machine learning can mine information in historical trajectory data for effective prediction, which is often reported to produce satisfactory results. Machine learning technology has been widely used in autonomous driving, image recognition, etc., in which deep neural networks show good application ability. Moreover, research on deep neural network classifiers [12] and the detection of backdoor attacks [13] was reported to further improve recognition performance. In terms of the trajectory prediction of aircraft, the trajectory predictors could be enhanced by learning from historical data [14]. For example, [PERSON] et al. proposed a constrained long short-term memory network for flight trajectory prediction.