# Intelligent Trajectory Prediction Algorithm for Reentry Glide Target Based on Intention Inference
###### Abstract
The prediction effect of the above methods is excellent, however, their combination with the dynamic model and the design of the network is not strong enough, and the training dataset lacks filtering processing. In short, for the current trajectory prediction methods based on intention inference, the analysis of associations among target, strategic places and no-fly zones is inadequate, and the influencing factors of intention are too simple to reflect the intention information. What is more, for the selection of control parameters and design of the target threat function, the guidance mechanism and flight data of reentry glide targets are not considered. Aimed at solving the above problems, this paper proposes the application of the target's initial and last state modification network, parameter modification network and parameter value prediction network to predict the value of control parameters according to the target-filtering data. Then, based on the relationship between the strategic place and the target, the importance map of strategic places is established, which is used to infer the attack position and calculate the importance of strategic places. The association model between trajectories and key points is constructed to calculate the time-to-go and importance changes. Then, according to the common guidance mechanism of reentry glide targets, the time-to-go, cross-range, longitudinal range and the relative importance values are taken as the influencing factors, which are used as a basis to establish the intention inference network by the deep temporal neural network. Finally, combined with the guidance logic, the predicted trajectory is obtained. The main innovations of this paper are as follows:  
1. According to the guidance characteristics of the reentry glide target, an initial and last state modification network, parameter modification network and parameter value prediction network are proposed to realize the prediction of target control parameter values;
2. The importance map of strategic places and the association model between key points and trajectories are proposed. On this basis, the relative importance, time-to-go, cross-range and longitudinal range can be further calculated, which better reflects the intention of the target;
3. The intention inference network of the target is designed by using the deep temporal network, and an explainable model of the threat function is learnt. Combined with the designed change rule of bank angle, the long-term trajectory prediction of the reentry glide target is realized. The content of this paper is arranged as follows. In Section 1, the research status of reentry glide target trajectory prediction is introduced, and the innovation points and arrangements of this paper are explained. Target dynamic and prediction models are introduced in Section 2. The structure of the parameter prediction network is outlined in Section 3, and the importance map of strategic places and the association model between key points and trajectories are described in Section 4. Then, in Section 5, the intention inference network and change rules of bank angle are designed. In Section 6, simulation is carried out to verify the validity of the proposed prediction method, the error is analyzed, and other trajectory prediction algorithms are compared with the proposed method. Finally, the paper is concluded in Section 7.