A segmented trajectory planning and guidance method for hypersonic glide vehicles considering target detection performance

This paper proposes a closed-loop detection and guidance method for optimizing the detection effectiveness of Radio Frequency (RF) stealth targets by terminal-guided hypersonic glide vehicles (HGV) during the terminal-guidance phase. This method divides the guidance process into two stages based on the detectability of the target. In the search stage, the Twin Delayed Deep Deterministic Policy Gradient (TD3) coupled with Successive Convex Optimization (SCO) guidance method is employed. This approach separates the detection performance from the numerical solution process of the optimization problem, enabling online adjustment of performance indicator functions to refine trajectory configurations. In the tracking phase, the dynamic characteristics of the target's Radar Cross Section (RCS) are convexified and discretized, incorporated into the objective function of the optimization problem as performance indicators. The Higher-Order Soft-Trust-Region Sequential Convex Programming (HSSCP) method is employed to improve the convergence of the algorithm. Additionally, the online trajectory planning problem is integrated into the Model Predictive Control (MPC) framework to achieve closed-loop guidance. Simulation results indicate that this method can improve the detection performance of RF stealthy targets during the terminal guidance phase, the cumulative detection information for the target increased by approximately 20.6%, and the maximum instantaneous detection probability improved by about 14.3%. Moreover, it can achieve convergence of complex multi-constraint problems within a limited number of iterations.