Aiming at the problem of the performance degradation of the traditional tracking algorithm when confronted with glint noise, a high performance filtering method named as IMM-CKF was proposed by integrating the CKF(cubature Kalman filter) into the framework of IMM(interacting multiple model). In the proposed algorithm, the target state was modeled as Gaussian distribution, the glint noise was modeled as Gaussian mixture distribution, and the occurrence probability of the glint noise was modeled as the first-order Markov process. An IMM framework was then used to implement model-matched filtering for each Gaussian component. To further mitigate the impact of nonlinear observation condition on tracking accuracy, the CKF was utilized as Gaussian approximation filter to realize recursive prediction and update of the target state. Simulation results show that the proposed method not only has higher tracking accuracy than traditional algorithms such as Gaussian sum filter and particle filter, but also has better real time ability. Additionally, the IMM-CKF can effectively estimate the existence of glint noise.