State Estimation and Trajectory Prediction of Near Space Hypersonic Vehicles

Near space hypersonic vehicles present severe challenges for real-time trajectory prediction due to their high dynamics, expansive operating domains, and unpredictable maneuvering patterns. This article proposes an online trajectory prediction method based on Gaussian Process (GP) regression. Unlike existing approaches, the proposed method does not require complex pretraining or extensive prior knowledge, yet it achieves an effective balance between high accuracy and real-time performance. Specifically, the method first employs an extended Kalman filter (EKF) to fuse the vehicle's fast- and slow-time-varying kinematic models, yielding an efficient online estimate of acceleration. This acceleration estimate sequence is then used to train the GP model, enabling rolling prediction of future accelerations within a sequential iterative framework. Simulation results in two representative maneuvering scenarios demonstrate that the proposed method accurately captures the vehicle's motion trends from the online estimates and delivers superior real-time prediction accuracy.