关键词: 高超声速滑翔飞行器, 气动加速度估计, 跟踪滤波, 长短时记忆网络, 轨迹预测

Abstract: The near-space hypersonic gliding vehicle (HGV) poses a significant threat to modern defense systems due to its ultra-high velocity, extreme maneuverability, and superior penetration capabilities. To address the challenges in tracking and predicting HGV trajectories for interception, this study introduces an intelligent trajectory prediction method based on aerodynamic acceleration estimation. By systematically analyzing the HGV motion model, maneuver patterns, and aerodynamic variation laws, three critical parameters—aerodynamic lift acceleration, drag acceleration, and bank angle control—are identified as key trajectory prediction variables, replacing unknown terms in the HGV motion model. A dynamics tracking model incorporating aerodynamic acceleration estimation is developed, utilizing radar measurement data and the Unscented Kalman Filter (UKF) for real-time tracking and estimation of these parameters. These estimated parameters are then used as inputs to train a Long Short-Term Memory (LSTM) network, which captures temporal relationships and variation patterns in the prediction parameters. The trained LSTM network iteratively forecasts future aerodynamic accelerations, which are integrated with numerical solutions of motion equations to extrapolate HGV trajectories. Numerical simulations confirm that the proposed method achieves high prediction accuracy and robust stability in predicting trajectories of non-cooperative HGVs.

Key words: hypersonic glide vehicle, aerodynamic acceleration estimation, tracking filter, LSTM network, trajectory prediction