New nonlinear filtering algorithm for ballistic target tracking
Chongzhao Han · Computer Engineering and Applications Journal · 2011
This paper studies the problem of tracking a ballistic target in the reentry phase.It proposes an adaptive algorithm,Strong Tracking Finite-Difference Extended Kalman Filter(STFDEKF),for ballistic target tracking in reentry.This method uses polynomial approximations obtained with a Sterling interpolation formula to approximate the derivative of the nonlinear function,and uses strong tracking factors to modify the prior covariance matrix.The proposed algorithm improves the tracking accuracy,enlarges the applied area and enhances the filtering convergence.It compares the performance of the proposed algorithm with that of the Extended Kalman Filter(EKF) and the Finite-Difference Extended Kalman Filter(FDEKF) using a Monte Carlo simulation.The simulation results show that STFDEKF outperforms EKF and FDEKF in terms of tracking accuracy,filter credibility and robustness against the sensitivity to filter initial condition.It concludes that the STFDEKF is an effective algorithm for the ballistic target tracking problem being studied.