In order to solve the nonlocal sampling problem inherent in the cubature kalman filter(CKF) algorithm for high dimensional problems, a methodology based on orthogonal transformation on the cubature points is proposed. Firstly, the unscented Kalman filter(UKF) algorithm and CKF algorithm are deduced from the perspective of numeriacal integration in the gaussian filtering framwork, and it is pointed out that the CKF is virtually a spacial case of the UKF. Then, the performance of the unscened transform(UT) is analyzed based on the multi-dimensional Taylor series, it reveals that the problem of numerical instability of the UKF can be solved by using the CKF, meanwhile the nonlocal sampling problem is introduced. Finally, through the orthogonal transformation of the sampling point in the CKF algorithm, the TCKF algorithm is derived. It is proved theoretically that the TCKF algorithm has higher estimation accuracy than the CKF algorithm in the high-dimentional and strongly nonlinearity situation where local sampling problems are prominent. simulation examples verify the effectiveness of the proposed algorithm.