## 1 Introduction  
The re-entry glide vehicle [1] with a lift body structure has extremely fast re-entry and glide speeds and can make a jump maneuver of tens of kilometers in the longitudinal direction with hundreds of kilometers of maneuvering range in the lateral direction, which is difficult to predict and intercept [2], and thus has high research value. Research on the trajectory generation mechanism of re-entry glide vehicles is beneficial to generating more effective re-entry trajectories and improving guidance accuracy. In general, the trajectory generation methods of re-entry glide vehicles are mainly divided into predictor-corrector guidance and nominal trajectory guidance. At present, the numerical predictor-corrector method is adopted within the mainstream predictor-corrector guidance methods, and the process is mainly divided into longitudinal and lateral guidance. Longitudinal guidance is primarily realized by setting the attack angle profile and using the secant method [3], the landing points method [4], or the flight range prediction network [5],to calculate the value of the bank angle. In the lateral direction, in order to consider the guidance and the avoidance of the no-fly zones, the avoidance logic [6] is usually combined with the heading deviation angle corridor [7] and cross-range corridor [8] when the adaptive cross-range corridor [5] integrating the two functions is also used to realize guidance. In general, although the trajectory generation speed of the predictor-corrector algorithm is fast, its guidance accuracy can be further improved. At the same time, the nominal trajectory generation of a re-entry glide vehicle is a complex nonlinear problem. The primary method is transforming the problem of re-entry trajectory generation into the optimization problem of discrete points through the sequential convex optimization method [9]. In recent years, several types of research have been performed. [PERSON] [10] et al. and [PERSON] [11] et al. solved the multiple constraint re-entry optimization problem based on the sequential quadratic programming (SQP) method through linearization and variable relaxation. [PERSON] [12] et al. treated the online trajectory generation problem of re-entry glider aircraft as a second-order cone programming problem and designed the corresponding optimal feedback tracking guidance design for effective tracking. [PERSON] [13] et al. realized continuous guidance of re-entry vehicles based on the convex optimization algorithm and continuous closed-loop control. [PERSON] [14; 15] et al. introduced a nonlinear relaxation method, which can reduce the relaxation degree, and the effectiveness of the method is verified. [PERSON] [16] et al. used the convexity method to provide a high-quality initial trajectory guess for the pseudo-spectral method. All the above methods realized the convexification of the re-entry dynamic model and constraints of the vehicle in the time domain and obtained the generated re-entry trajectory. [PERSON] [17] et al. compared the optimal trajectories using [PERSON]'s method and the slew trajectories using sinusoidal functions, and approximately 1.5% lower control effort was obtained. Then, based on [PERSON]'s approach, [PERSON] [18] established an autonomous trajectory maneuver to de-orbit spacecraft back to Earth with a distributed waypoint for autonomous collision avoidance. However, according to the guidance mechanism, the planned spatial state of grid points is not directly reflected in the time domain, more states can be considered to describe the dynamic model. In order to improve the convergence and accuracy of trajectory generation based on convex programming, many improved methods have been proposed. [PERSON] [19] and [PERSON] [20; 21] introduced new variables to adjust the trust region in the aircraft planning process adaptively, and this can realize faster convergence. [PERSON] [22] et al.