Intelligent Hypersonic Gliding Vehicle Trajectory Prediction Based on Aerodynamic Acceleration Estimation

doi:10.12382/bgxb.2025.0035

Abstract

Abstract:

The near-space hypersonic gliding vehicle (HGV) poses a significant threat to existing defense systems due to its ultra-high velocity,extreme maneuverability,and superior penetration capabilities.To address the challenges in tracking and predicting HGV trajectories during interception,this paper presents an intelligent trajectory prediction method based on aerodynamic acceleration estimation.The maneuver patterns and aerodynamic variation laws of HGV are systematically analyzed according to the HGV motion model.On this basis,three critical parameters,i.e.,aerodynamic lift acceleration,drag acceleration and bank angle control,are identified as trajectory prediction variables for replacing the unknown terms in the HGV motion model.A dynamics tracking model based on aerodynamic acceleration estimation is developed to use the 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 the temporal relationships and variation patterns in the prediction parameters.The trained LSTM network is used to iteratively forecasts future aerodynamic accelerations,which are integrated with the 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 the trajectories of non-cooperative HGVs.

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

CLC Number:

TP13

YU Mingjun, ZHANG Jialiang, SHEN Haidong, LIU Yanbin, CHEN Jinbao. Intelligent Hypersonic Gliding Vehicle Trajectory Prediction Based on Aerodynamic Acceleration Estimation[J]. Acta Armamentarii, 2025, 46(6): 240035-.

Figures/Tables 32

Fig.1 Aerodynamic accelerations at different angles of attack

Fig.2 Radar observation model

Fig.3 Overall scheme of trajectory prediction method based on LSTM parameter identification

Fig.4 LSTM network-based trajectory recursive prediction

Fig.5 Data segmentation procedure

Fig.6 LSTM network structure

Fig.7 Simulation scene

Table 1 Neuron node number Settings

网络层	神经元个数
LSTM层1	64
LSTM层2	128
输入层1	64
输入层2	3

Table 2 Network training hyperparameter settings

参数	取值
最大训练迭代次数	100
最小批次	128
初始学习率	0.005
学习率下降因子	0.2
正则化系数	0.1
优化器	Adam

Fig.8 Loss during training of LSTM network

Fig.9 Tracking results of aerodynamic drag acceleration

Fig.10 Tracking results of aerodynamic lift acceleration

Fig.11 Tracking results of sinusoidal quantity of bank angle

Fig.12 Estimated result of target flight-path angle

Fig.13 Estimated result of target heading angle

Fig.14 Estimated result of target speed

Fig.15 Estimated result of target spatial location

Fig.16 Predicted results of aerodynamic drag acceleration

Fig.17 Predicted result of aerodynamic lift acceleration

Fig.18 Predicted results of sinusoidal quantity of bank angle

Fig.19 Predicted result of target flight-path angle

Fig.20 Predicted result of target heading angle

Fig.21 Predicted result of target speed

Fig.22 Variation of target trajectory longitude over time

Fig.23 Variation of target trajectory latitude over time

Fig.24 Variation of target trajectory height over time

Fig.25 Result comparison of different predicting methods

Fig.26 Results of Monte Carlo simulations

Fig.27 Monte Carlo simulation results of horizontal plane

Fig.28 RMSEs corresponding to Monte Carlo simulations

Table 3 Average online prediction time for three methods

方法	在线训练耗时/s	在线预测耗时/s
文献[29]方法		0.0089
文献[30]方法		0.2117
本文方法	13.6	0.2932

Fig.29 Tracking-decision-Pprediction timeline

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