Decades of improvements in re-entry ballistic vehicle tracking

Abstract

This paper deals with a missile defense challenge, which has been studied extensively for decades and still remains a topic of active research, the tracking of a ballistic vehicle in its re-entry phase. With Anti-Ballistic Missile or Anti-Tactical-Ballistic Missile goals, most of missile defenses need to track re-entry vehicles with a view to locating them precisely and allowing low altitude interception. The re-entry vehicle leaves a quiet exoatmospheric phase and a quasi-Keplerian motion to an endo-atmospheric phase with large aerodynamic loads and a sudden deceleration. The motion is then obviously nonlinear and furthermore both the extent and the evolution of the drag are difficult to predict. Since the sixties, while more and more efficient sensors, such as sophisticated phased-array radars, were developed, the associated data processing techniques have been improved, taking advantage of computer performance increases. Although re-entry vehicle tracking is undoubtedly a non-linear filtering problem, it was firstly solved by rustic linear filters with fixed weight and an assumed drag table. Then, Kalman filters were exploited. Uncoupled and polynomial for a while, they are now fully coupled and really non-linear. They are called Extended Kalman filters, are based on the linearization about the estimated state and are the current efficient and classic solution to non-linear filtering. Moreover, they perform drag estimation which may be useful to the identification capabilities of the missile defense. In the future, even more sophisticated techniques, such as the promising and fashionable particle filtering, are likely to be developed in re-entry vehicle tracking.