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.
Tracking a ballistic target: comparison of several nonlinear filters
IEEE Transactions on Aerospace and Electronic Systems ; 38 , 3 ; 854-867
2002
14 Seiten, 25 Quellen
Article (Journal)
English
Tracking a Ballistic Target: Comparison of Several Nonlinear Filters
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