### HGV maneuver Intention Prediction  
HGVs typically use the boost-glide-dive flight mode, in which the vehicle is in the gliding phase during most of the flight time [2; 3; 4]. The gliding of HGVs is non-inertial, with higher flight speed, lower flight altitude, and stronger maneuverability than inertial trajectory targets [5]. Numerous scholars and laboratories have researched to solve the flight track prediction problem of HGVs. The main research directions include adaptive filtering algorithms [6; 7; 8; 9; 10; 11] and interacting multiple model algorithms. For actual trajectories that are comparable to the model's trajectory, the single-model adaptive filtering algorithm has a high prediction accuracy [12]. However, it has a low prediction accuracy for trajectories with high uncertainty and different combinations of maneuver sequences. The interacting multiple model algorithm (IMM) [13] can infer similar models in the established model set based on the target observations and their associated computational quantities. Suitable models are selected for track prediction in different flight phases of the target. At present, the geographic location model and aerodynamic model of HGVs are well established, which can constitute the conventional maneuver model of HGVs [14; 15]. In order to adapt the model set to all possibilities of the maneuver sequences, the number of models in the model set becomes increasingly large, and the complexity of the probability transfer matrix required for model selection increases. As a result, computational speed and dynamic performance are degraded. The probabilistic transfer matrix cannot accurately describe the uncertainties of the target maneuver strategy, so the interacting multiple model algorithm gradually becomes difficult to adapt to the increasingly complex maneuver form of HGVs. Therefore, in recent years, machine learning approaches have been increasingly adopted to solve the trajectory prediction problem for HGVs [16; 17]. Herein, a convolutional neural network (CNN) and a long short-term memory network (LSTM) are combined into a classification network to classify the trajectories of HGVs. Then prediction networks such as LSTM [18], gated recurrent units (GRU), and bidirectional recurrent neural networks (Bi-RNNs) [19] are applied to the trajectory prediction problem of HGVs in the absence of a large set of models. A satellite constellation tracking system relies on the calculations of the HGVs trajectory prediction algorithm for mission planning. Although the research on the trajectory prediction of HGVs has become increasingly mature, the uncertainty problem of maneuver sequences still has a large impact on trajectory prediction and tracking mission planning [20; 21; 22]. Therefore, the study of the uncertainty problem of maneuver intention sequences of HGVs has been gradually developed [23]. Theoretical trajectory generation of HGVs consists of design, optimization, and control processes, and each of its maneuvers is purposeful. Maneuver intentions can be divided into two categories according to the direction: longitudinal maneuver intentions and lateral maneuver intentions. These two types of maneuver intentions each have certain laws and are strongly coupled with each other. A recurrent neural network (RNN) is a chain network structure consisting of connected recurrent units [24]. RNN is capable of handling time sequence information problems, but there is gradient disappearance and gradient explosion during the training process. Long short-term memory (LSTM) is a variant of the RNN with a special gate mechanism. It effectively solves the gradient disappearance problem that occurs during the training of traditional RNN for long time sequences [25]. The LSTM network provides feature extraction, memory functionality and high-dimensional information processing [26]. Moreover, the stacking layer mechanism can enhance the power of the LSTM to cope with more complex recognition and prediction problems of temporal correlation.