Comparison of recursive and batch processing for impact point prediction of ballistic targets

The paper deals with the problem of impact point prediction of ballistic targets (BT) by processing measurements acquired either by 3D surveillance or multifunctional phased-array radars. It is assumed that the radar acquires a limited number of measurements that do not encompass the whole target trajectory; thus the established target track has to be extrapolated ahead in time in order to predict the coordinates of the impact point. In this paper we compare performance of batch (i.e. maximum likelihood estimation, MLE) and recursive (extended Kalman filter EKF and unscented Kalman filter UKF) filtering techniques.

[1]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[2]  S. Immediata,et al.  Detection and tracking of ballistic target , 2004, Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509).

[3]  A. Farina,et al.  Estimation accuracy of a landing point of a ballistic target , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[4]  Branko Ristic,et al.  Cramer-Rao bound for nonlinear filtering with Pd<1 and its application to target tracking , 2002, IEEE Trans. Signal Process..

[5]  Yaakov Bar-Shalom,et al.  Estimation and Tracking: Principles, Techniques, and Software , 1993 .

[6]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[7]  A. Farina,et al.  Tracking a ballistic target: comparison of several nonlinear filters , 2002 .