Iterated divided difference filter and its applications
Yuanli Cai · Kongzhi yu juece · 2011
The linearization error,which is propagated in the measurement update of the extend Kalman filter(EKF) and iterated extended Kalman filter(IEKF),may somewhat degrade the filtering performance.Therefore,an iterated divided difference filter is proposed,which combines the iterated method and statistically linearization error propagation with the divided difference filter.This algorithm is applied to state estimation for reentry ballistic target.Simulation results show that,the proposed method can reduce the in?uence of the measurement nonlinearity,and higher accuracy of state estimation is guaranteed.