Abstract: Research on target tracking with glint noise is important to improve the detection performance of sensor, in which the glint noise’s nonGaussian property has puzzled researchers for a long time. To overcome this problem, the cubature particle filter’s performance deficiency in target tracking with the glint noise is theoretically analyzed. Then, a algorithm called Gaussian sum cubature Kalman filter (GSCKF) is proposed. Based on the methodology of Gaussian sum filter (GSF) and the cubature Kalman filter(CKF), the proposed algorithm models the nonGaussian noise and the state posterior distribution as finite weighted Gaussian mixture, and a bank of CKF is running in parallel where the filtering and predictive distributions are updated by using the CKF equations. Moreover, the proposed algorithm utilizes the model reduction techniques to limit the number of Gaussian components, thus it is suitable for nonlinear and nonGaussian state estimation. In order to compare the performance of the two nonGaussian algorithms, comparative experiments between GSCKF and CPF from the three aspects of tracking accuracy, robustness and computational complexity are carried out.