1. INTRODUCTION
Popular nonlinear filters for state estimation include the extended Kalman filter (EKF) [1], [2], the unscented Kalman filter (UKF) [3], and the particle filter (PF) [4]. In practice, the EKF is widely used due to its much lower computational load than the PF and UKF. In the motion and measurement models of a non-linear filter, noise terms are used for compensate the errors made by: (1) model simplification, (2) additional states not modeled, (3) discretization error, (4) model linearization, and (5) Gaussian noise assumptions. However, it takes significant amount of time and man power to manually fine-tune the EFK and acquire proper system noise statistics used by the filter. It is thus desirable to develop an approach that automatically finds best noise statistics for an EKF.