Adaptive interactive multiple model target tracking algorithm based on Markov matrix with acceleration correction factor

In the field of target tracking, the interactive multimodel (IMM) algorithm is a very effective method. To improve the tracking performance, This paper proposes an improved current statistical model (CS) combined with the IMM algorithm and an improved model set to implement the model set adaptive AIMM-CS algorithm. At the same time, to address the problem of slow model switching and reduced tracking accuracy caused by the Markov matrix given by a priori knowledge and always constant, this paper obtains a correction factor from the estimated value of acceleration and corrects the Markov matrix. Experiments show that the algorithm in this paper can effectively improve the tracking accuracy and increase the model switching speed.