Predicting Hypersonic Glide Vehicle Behavior With Stochastic Grammars.

Hypersonic glide vehicles are a new class of vehicles that fly at hypersonic speeds and have high maneuverability. These fast-moving targets exhibit different flight characteristics compared to conventional vehicles, so traditional tracking and defense systems require new methods to contend with them. In this paper, we propose a machine learning method for predicting the behavior of hypersonic glide vehicles. Our method is based on a stochastic grammar, which is a mathematical framework that describes the possible transition patterns of sequences. We use the stochastic grammar to predict the transition patterns in hypersonic glide vehicle trajectories. Given a partial trajectory, our method uses the grammar to predict the hypersonic glide vehicle's future kinematics, such as its altitude, velocity, and acceleration. We evaluate our method on two datasets of simulated hypersonic glide vehicle trajectories and show that it can successfully predict hypersonic glide vehicle behavior, even in the presence of noise. We also show that our method can predict several minutes into the future and can accurately predict future hypersonic glide vehicle behavior based on shorter observation times. Our results suggest that our method has the potential to be a valuable tool for predicting behavior of hypersonic glide vehicles.