Re-entry Vehicle Tracking Using Adaptive Extended Kalman Filter from Discrete Noisy Measurements

Tracking of a ballistic vehicle (BV) flying in the Earth’s atmosphere by radar is a very complex issue to cope with and needs some (suboptimal) nonlinear filtering techniques for it. Further, if some characteristics or parameters of the vehicle are poorly known or unknown, it adds an identification problem to the tracking problem. In this paper, an adaptive extended Kalman filter (AEKF) is proposed for solution of tracking problem of a ballistic vehicle by processing radar measurements in the re-entry phase of its motion. The proposed AEKF algorithm is tested and examined for a typical BV tracking problem adopted from literature. In the current study, vehicle tracking problem has been formulated as (i) state estimation problem and (ii) joint parameter and state estimation problem. The effectiveness of the proposed AEKF algorithm is compared with most accepted extended Kalman filter in terms of estimation correctness and computational effort. The simulation results of the present work prove the potential of the AEKF in solving a non-linear re-entry phase target tracking problem.