To settle the problem of low tracking precision caused by fixed Transition Probability Matrix (TPM) of standard Interacting Multiple Model (IMM) algorithm, an Adaptive IMM (AIMM) algorithm of TPM is introduced. Based on the probability change information of adjacent time and the posterior probability information of different models at current time, an element correction function of Markov matrix is introduced to adjust each element in the transition probability matrix in real time. Besides, Cubature Kalman Filter (CKF) is introduced into IMM algorithm. The simulation experiments are set to testify the availability of the proposed algorithm. The results show that in comparison with the traditional IMM-CKF algorithm and other adaptive IMM algorithms, the proposed AIMM-CKF algorithm effectively improves model matching probability and model switching rate, which exhibits better tracking effects.