Tracking a ballistic object on reentry: performance bounds and comparison of nonlinear filters

Abstract

Tracking of a ballistic reentry object from radar observations is a highly complex problem in nonlinear filtering. We derive the Cramer-Rao lower bounds (CRLBs) for the variance of the estimation error for this problem. Subsequently we compare several nonlinear filtering techniques to the derived CRLBs. The considered nonlinear filters include the extended Kalman filter, the unscented Kalman filter and the bootstrap (particle) filter. Considering the computational and statistical performance, the unscented Kalman filter is found to be the preferred choice for this application.