Exploring a better IMM-UKF fusion algorithm based on current statistical model in target tracking

The introduction of the full paper reviews a number of relevant papers in the open literature and points out that there is an urgent need to explore; but, to our knowledge, there is no paper in the open literature that explores the effects of interacting multiple models and unscented Kalman filtering (IMM-UKF) fusion method. Sections 1, 2 and 3 explain the result of our exploration, which is the design of our IMM-UKF fusion algorithm based on the current statistical model and which we believe is better than previous ones. The rest of the core of sections 1, 2 and 3 is that the probability of the current statistical model is calculated and that the calculation method combines the advantages of the current statistical model with those of IMM-UKF fusion algorithm so as to extend the applicability range of the current statistical model. The simulation results, given in Figs. 2 through 8, and their analysis show preliminarily that our IMM-UKF fusion method can indeed effectively track the real-time maneuvering target and reduce the average value of errors and their standard deviation value and improve the convergence speed and tracking precision.