Prediction and Control of Projectile Impact Point using Approximate Statistical Moments
For engineers designing projectile guidance systems, this work offers a computationally feasible method for stochastic prediction and control, though it is incremental as it applies known moment approximation techniques to a specific domain.
The paper develops a stochastic model for projectile trajectory prediction and control under noise, using approximate statistical moments to estimate impact point mean and standard deviation, and derives a control law that reduces hit error.
In this paper, trajectory prediction and control design for a desired hit point of a projectile is studied. Projectiles are subject to environment noise such as wind effect and measurement noise. In addition, mathematical models of projectiles contain a large number of important states that should be taken into account for having a realistic prediction. Furthermore, dynamics of projectiles contain nonlinear functions such as monomials and sine functions. To address all these issues we formulate a stochastic model for the projectile. We showed that with a set of transformations projectile dynamics only contains nonlinearities of the form of monomials. In the next step we derived approximate moment dynamics of this system using mean-field approximation. Our method still suffers from size of the system. To address this problem we selected a subset of first- and second-order statistical moments and we showed that they give reliable approximations of the mean and standard deviation of the impact point for a real projectile. Finally we used these selected moments to derive a control law that reduces error to hit a desired point.