A sensitivity analysis of extended kalman filter for gps position estimation with and without clock offset

Abstract

Extended Kalman Filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However EKF is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise due to its linearization form. This paper focuses on improving accuracy a few meters for navigation solution using EKF algorithm. Real time GPS raw data acquired by tracing a trajectory is further analyzed in a post-mission processing approach. The algorithm is used to evaluate GPS measurements with and without clock offset (clock error) and to show differences in accuracy. Results show that an improvement in the receiver position estimation by minimizing the error by a factor of 1 m to 3 m, if clock offset parameter is considered. Even though the clock offset parameter is ignored, EKF is able to estimate GPS receiver position with more error.