Abstract: Aiming at the drawbacks of the extended Kalman filter (EKF) which is the widely used GPS frequency estimation algorithm in high dynamic circumstance, a novel filtering algorithm called simplified unscented Gaussian particle filter (SUGPF) was proposed. The SUGPF is the combination of Kalman filter (KF), unscented Kalman filter (UKF) and Gaussian particle filter (GPF). In time update step, KF methodology was used to update the predictive distributions. In measurement update step, the UKF methodology was used to obtain the important sampling function, and the posterior distributions were updated by using the methodology of GPF. The simulation results indicate that the SUGPF has improved performance and versatility over the EKF and UKF, under both Gaussian and non-Gaussian observation noise condition, SUGPF can achieve good performance which is similar as that of the GPF, and the computational complexity of the SUGPF is lower than that of the GPF.