Re-Entry Gliding Vehicle Trajectory Prediction Based on Maneuver Detection

Re-entry gliding vehicles exhibit high maneuverability, making trajectory prediction a key factor in the effectiveness of defense systems. To overcome the limited fitting accuracy of existing methods and their poor adaptability to maneuver mode mutations, a trajectory prediction method is proposed that integrates online maneuver mode identification with dynamic modeling. Characteristic parameters are extracted from tracking data for parameterized modeling, enabling real-time identification of maneuver modes. In addition, a maneuver detection mechanism based on higher-order cumulants is introduced to detect lateral maneuver mutations and optimize the use of historical data. Simulation results show that the proposed method achieves accurate trajectory prediction during the glide phase and maintains high accuracy under maneuver mutations, significantly enhancing the prediction performance of both three-dimensional trajectories and ground tracks.