A comparison of multiple-IMM estimation approaches using EKF, UKF, and PF for impact point prediction

We discuss a procedure to estimate the state of thrusting/ballistic endoatmospheric projectiles for the purpose of impact point prediction (IPP). The short observation time and the estimation ambiguity between drag and thrust in the dynamic model motivate the development of a multiple interacting multiple model (MIMM) estimator with various drag coefficient initializations. In each IMM estimator used, as the mode-matched state estimators for its thrusting mode and ballistics mode are of unequal dimension, an unbiased mixing is required. We explore the MIMM estimator with unbiased mixing (UM) using extended Kalman filter (EKF), unscented Kalman filter (UKF) and particle filter (PF). For 30 real trajectories, the IPP based on the MIMM-UM estimation approach is carried out with various sets of tuning parameters selected. The MIMM-UM-EKF, MIMM-UM-UKF and MIMM-UM-PF are compared based on the resulting IPP performance, estimator consistency and computational complexity.

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