Synthetic Hypersonic Glide Vehicle Trajectories for Inverse Control Reconstruction and Terminal Impact Prediction
Year: 2026
Type: dataset
Abstract: This dataset contains synthetic hypersonic glide vehicle (HGV) trajectory data generated using a deterministic three-degree-of-freedom (3-DOF) point-mass simulation framework. The simulations incorporate Earth's rotation, gravitational perturbations through the J4 zonal harmonic, the 1976 U.S. Standard Atmosphere, and aerodynamic coefficient models based on the Common Aero Vehicle (CAV-H). The data support a two-stage machine-learning framework for reconstructing trajectory control parameters and predicting terminal impact locations from a single time-tagged kinematic state at the final skip-glide extremum. The dataset comprises two components: 1. Inverse control reconstruction: 71,676 labeled state observations extracted from 960 simulated trajectories. Observations are sampled at skip-glide altitude extrema and intermediate states and are associated with angle of attack, bank angle, and deorbit velocity increment. 2. Terminal impact prediction: 21,576 simulated trajectories, each contributing a transition-state observation at the final skip-glide maximum and a corresponding terminal impact location. These data support regression of the great-circle distance and bearing between the transition point and terminal impact. The sampled control parameters span angles of attack from 4° to 11°, bank angles from −15° to 15°, and deorbit velocity increments from 0.09 to 0.15 km/s. The dataset is intended to support reproducibility, independent analysis, and further research into physics-informed machine learning, inverse trajectory modeling, and surrogate-based impact prediction. All observations are simulation-generated under deterministic assumptions without measurement noise and should not be interpreted as operational flight measurements.
Source: Mendeley Data
DOI: 10.17632/h6fbbh6j74
Institution: University of Alabama
Language: English
Topic: Guidance and Control Systems
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225061758