# Intelligent Trajectory Prediction Algorithm for Reentry Glide Target Based on Intention Inference
###### Abstract
[PERSON] et al. [13] accomplished trajectory prediction by analyzing the maneuvering mode of the target, generating a corresponding training set, training a support vector machine classifier, and combining it with an extended Kalman filter algorithm. However, the above methods only have good effects on trajectory prediction in partial maneuvering modes, while using training sets or networks to predict trajectories lacks a modeling basis, which may reduce the prediction accuracy and lead to the rapid divergence of prediction errors  
The above two types of trajectory prediction methods mainly analyze the observation information of the target to realize prediction, which is insufficient in the modern situation analysis of strategic places and guidance mechanism of the target and may accelerate the accumulation of long-time trajectory prediction errors. Therefore, introducing auxiliary information for trajectory prediction is a useful method to improve prediction accuracy. As auxiliary information, the attack intention of the target plays an important role in improving prediction accuracy and has been widely used in aircraft trajectory prediction. [PERSON] et al. [14] proposed a trajectory predictor based on the interacting multiple model (IMM) algorithm guided by the spatial and temporal information in the intent. Inspired by search strategies used in artificial intelligence, [PERSON] et al. proposed a novel approach in which a search tree is generated to evaluate possible intent maneuvers at each point [15]. Further analyses of target attack intentions to realize the trajectory prediction of reentry glide targets have a good application value. [PERSON] et al. [16] discussed the key technology of reentry glide target trajectory prediction from the four aspects of establishing a prediction model, correcting the prediction error, identifying the guidance law, and inferring the target intention, and proposed a trajectory prediction theory combining state information and attack intention. Accordingly, based on the assumption that target intention is our strategic places, the maneuvering mode of the target is deduced based on Bayesian theory, and trajectory prediction is realized based on Monte Carlo sampling [17]. [PERSON] et al. [18] used an autoregressive model to identify maneuver modes, combined target intention with Bayesian theory to obtain the target's state iteration dynamic equation, and selected prediction methods based on the effects. Combined with relevant intention information, the above methods further simplify the prediction model and effectively real ize trajectory prediction. However, the uncertainty of the Bayesian process and sampling method may produce errors. With the development of deep learning technology, fitting complex functions using a deep network has a favorable effect. In particular, recurrent neural networks (RNN) and long- and short-term memory networks (LSTM) [19] have beneficial effects on sequential feature extraction and sequential rule learning [20]. Trajectory prediction methods based on deep neural networks have also been widely applied in the field of aircraft trajectory prediction [21; 22]. Recently, the temporal network has also been used in the trajectory prediction of reentry glide targets. [PERSON] et al. [23] designed a trajectory classifier and predictor based on the LSTM network and realized the trajectory prediction of non-ballistic targets driven by pure data. [PERSON] et al. [24] designed a gated recurrent unit (GRU) network based on a double-channel network that generates a prediction data set and carried out the trajectory prediction of high-speed targets. Using deep learning, [PERSON] el al. [25] identified the HGV's motion state according to state information then the appropriate prediction scheme was adopted to predict the trajectory robustly. [PERSON] et al. [26] used methods from natural language processing to model the flight phases and trajectories, and learned the grammar from the HGV trajectories to realize prediction.