Novel extended Kalman filter with linear-correction

This paper considers the divergence problem of the extended Kalman filter(EKF) in performing estimation of the state of a nonlinear system. A linear-correction EKF algorithm that involves closed-form weighted least squares(WLS) optimization only is developed using the time difference of arrival(TDOA) and frequency difference of arrival(FDOA) measurements The proposed method first estimates and subtracts the initial state estimation error in the result of EKF using the WLS approach, then recycles the filtering process again to overcome the error caused by the part linearization operation for the measurement matrix in EKF. Simulation results demonstrate the good performance of the proposed method.