Tracking a ballistic target: comparison of several nonlinear filters

TLDR

The study addresses tracking a ballistic reentry object using radar measurements. A highly nonlinear ballistic motion model is developed, its Cramer‑Rao lower bounds derived, and the estimation performance of EKF, statistical linearization, particle filtering, and UKF is compared. Simulations indicate that the EKF outperforms the other filters, delivering statistical efficiency with modest computational load when the ballistic coefficient is known a priori.

Abstract

This paper studies the problem of tracking a ballistic object in the reentry phase by processing radar measurements. A suitable (highly nonlinear) model of target motion is developed and the theoretical Cramer-Rao lower bounds (CRLB) of estimation error are derived. The estimation performance (error mean and standard deviation; consistency test) of the following nonlinear filters is compared: the extended Kalman filter (EKF), the. statistical linearization, the particle filtering, and the unscented Kalman filter (UKF). The simulation results favor the EKF; it combines the statistical efficiency with a modest computational load. This conclusion is valid when the target ballistic coefficient is a priori known.