Second-order Divided Difference Filter with Application to Ballistic Target Tracking

In this paper, we propose a second-order divided difference (DD2) filter for ballistic target tracking in the re-entry phase. The DD2 filter linearizes the nonlinear dynamic and measurement functions by using a second-order interpolation formula to approximate the derivatives of nonlinear functions. We compare the new method, extended Kalman filter (EKF) and unscented Kalman filter (UKF) using a Monte Carlo simulation approach. The simulation results show that DD2 filter outperform EKF and UKF in terms of estimation accuracy and filtering credibility. We conclude that the DD2 filter is a strong candidate for the ballistic target tracking problem being studied.