Tracking a ballistic target: comparison of several nonlinear filters

Author

Farina, A. ; Ristic, B. ; Benvenuti, D.

Author_Institution

Alenia Marconi Syst., Italy

Volume

38

Issue

3

fYear

2002

fDate

7/1/2002 12:00:00 AM

Firstpage

854

Lastpage

867

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.

Keywords

Kalman filters; ballistics; missiles; nonlinear filters; radar tracking; statistical analysis; target tracking; Cramer-Rao lower bounds; ballistic target; computational load; consistency test; error mean; estimation error; extended Kalman filter; nonlinear filters; particle filtering; radar measurements; reentry phase; standard deviation; statistical linearization; target ballistic coefficient; target motion; unscented Kalman filter; Australia; Covariance matrix; Estimation error; Measurement errors; Nonlinear equations; Nonlinear filters; Radar measurements; Radar tracking; Satellites; Target tracking;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/TAES.2002.1039404

Filename

1039404