This paper studies the centralized ultra-tightly integrated measurement model, and studies the ultra-tight integration filtering algorithm on the basis of the measurement model. Due to the characteristics of centralized ultra-tight integration with strong nonlinear, high dimension of state vector, the large amount of calculation, this paper applies a reduced-dimension cubature kalman filter (RCKF) algorithm to the centralized ultra-tight integration. Comparing with conventional CKF, only the elements in the state vector related to the nonlinear measurement information are sampled. As a result, the RCKF algorithm reduces the dimension of the sampling vectors, which reduces the calculation amount. The simulation shows that the RCKF algorithm can greatly reduce the amount of calculation in integrated filter without loss of accuracy.