Strong Tracking Finite-Difference Extended Kalman Filtering for Ballistic Target Tracking

摘要

This paper studies the problem of tracking a ballistic target in the reentry phase. We propose 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. We compare 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 and filter credibility, although it has higher computational cost. We conclude that the STFDEKF is an effective algorithm for the ballistic target tracking problem being studied.