To solve the problem of model switching slowness in interactive multiple model (IMM) algorithm when the target is maneuvering, an adaptive IMM (AIMM) algorithm with Markov probability transfer matrix online correction is presented. The Markov probability transfer matrix is adjusted by the probability difference between two consecutive moments in the IMM submodel to improve the switching speed and the rationality of the assignment of the submodel, and the tracking accuracy is improved. Secondly, squareroot volumetric Kalman filter (SRCKF) is introduced into AIMM algorithm to solve the nonpositive definite problem of covariance matrix and improve the numerical stability during iterative filtering. AIMMSRCKF algorithm for maneuvering target tracking is proposed. The simulation results show that the algorithm can improve the probability of matching models and shorten the model switching time.