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The strong tracking idea is introduced into the cubature Kalman filter (CKF), and the strong tracking CKF can effectively overcome the performance degradation problem of CKF under the condition of model uncertainty and state mutation. By analyzing the existing multiple fading factor calculation methods, it is found that they only use the diagonal elements of the covariance matrix, and do not consider the correlation between the states, and cannot give full play to the advantages of multiple fading factors. Aiming to solve this disadvantage, this paper proposes a novel fading factor matrix, based on the orthogonal principle to derive the solution method of the fading factor matrix, and proposes a multiple fading factors strong tracking CKF algorithm. The multiple fading factors strong tracking CKF algorithm breaks through the limitation of the traditional multiple fading factor as a vector, and does not require the value of the fading factor to be greater than 1. The simulation verifies that the algorithm has better filtering accuracy and robustness, and can better meet the requirements of engineering applications.