# Intelligent Trajectory Prediction Algorithm for Reentry Glide Target Based on Intention Inference
## 3 Parameter Value Prediction Network
### Association Models between Strategic Places and Trajectory
#### 4.2.2 Realization Method
\begin{array}{ll}\beta_{k}\leq\beta_{br},&\quad\text{if}\;\;t_{t} \geq t_{\beta r}\\ \beta_{l}\leq\beta_{k}\leq\beta_{r},&\quad\text{if}\;\;t_{t}\geq\min(t_{ \beta l},t_{\beta r})\\ \beta_{k}\leq\beta_{l},&\quad\text{if}\;\;t_{t}\geq\min(t_{\beta l},t_{\beta r })\end{array} \right. \tag{9}\]  
where \(\beta_{r}\) is the bank angle of a trajectory that is tangent to the no-fly zones from the right, and \(\beta_{l}\) is the bank angle of a trajectory which is tangent to the no-fly zones from the left. The parameters \(t_{\beta l},t_{\beta r}\) represent the tangent time from the right and left, respectively. (4) Estimation of time-to-go for key points within mapping corridor. If the key points are within the mapping corridor, the time-to-go of key points should be refreshed. According to step (3), the trajectory of bank angle \(\beta_{b}\) that integrates to time \(t_{gb}\) can be obtained. If the heading deviation angle \(|\Delta\psi_{k}|\) between the target at this time and key point k is less than the setting threshold value \(|\varepsilon_{\psi}|\), setting the bank angle as \(\beta=0\), let the target fly to the key point. Otherwise, \(|\Delta\psi|\geq\varepsilon_{\psi}\), the change law of bank angle is according to the following flying around rule,  
\[\beta_{t}=\left\{ \begin{array}{ll}\text{sgn}(\Delta\psi_{k})\text{arcsin} \left(\left|(r_{b}\rho K_{Lt})^{-1}\right|\right),&\quad\text{if}\;\;\text{ arcsin}\left(\left|(r_{b}\rho K_{Lt})^{-1}\right|\right)\leq 1\\ \pi\text{sgn}(\Delta\psi_{k})/2,&\quad\text{if}\;\;\text{arcsin}\left(\left|( r_{b}\rho K_{Lt})^{-1}\right|\right)>1\end{array} \right. \tag{10}\]  
Stop flying around until \(|\Delta\psi_{k}|<\varepsilon_{\psi}\), and record the cost time of flying around \(t_{c}\). Then, set the bank angle equal to \(0\) l, let the target fly to the key point until the distance between the target and key point \(d<\varepsilon_{d}\), and record the cost time \(t_{d}\). Therefore, the time-to-go  
Figure 6: The relationship between No-fly zones and Reachable region: (**a**) represents the left boundary of the no-fly zone intersects the reachable region; (**b**) represents the reachable region fully includes the no-fly zone; (**c**) represents the right boundary of the no-fly zone intersects the reachable region. of the target for key points with the mapping corridor of no-fly zones is refreshed as \(t_{t}=t_{gb}+t_{c}+t_{d}\). On the other hand, when the key point is out of the reachable region, the time-to-go for the key point will be \(t_{f}=\infty\). The dynamic effects are shown in Figure 7, where (a), (b), (c) and (d) represent the change process of dynamic association trajectories for different key points. The blue shaded area represents the no-fly zone mapping corridor, the gray shaded area represents the no-fly zones, and the blue dotted line represents the associated trajectory between the target and the key points. It can be seen that, based on the proposed model, the approximate lateral trajectories for key points can be obtained quickly, which can be used for the simple calculation of the time-to-go of the target. Some key points will gradually leave the target's reachable region, and their time-to-go reaches infinity. The proposed model can provide more auxiliary information for the later analysis of the attack intention of the target. Figure 7: Association model between key points and trajectories:(**a–d**) represent the association models at 0 s, 40 s, 90 s and 120 s respectively.