There are two shortcomings in the standard interacting multiple model (IMM) algorithm:one is that designing models is difficult

the other is that the application of constant transition probability matrices makes the model switching speed slow and tracking accuracy decreased.To overcome these shortcomings

an IMM algorithm with adaptive transition probability is proposed.Firstly

a new model-set design method is proposed

and the strong tracking modified input estimation (STMIE) model and constant velocity (CV) model are adopted as the model sets of the IMM algorithm.By using the capability of STMIE model to track high maneuvering targets and the precision of CV model to track non-maneuvering targets

this algorithm can be comprehensively adaptive in target tracking.Secondly

a new method is proposed to modify the Markov transition probability in real time based on the likelihood values of the models

which enhances the effect of the matching model

and weakens the influence of the mismatched model.Simulation results show that the new method improves model switching speed and tracking precision of IMM algorithm

and the tracking precision of IMM-STMIECV algorithm is higher than that of IMM-CVCA

IMM-CVCACT and IMM-CVCS algorithms.