A Comparison of Interactive Multiple Modeling Algorithms for Maneuvering targets tracking

Interacting Multiple Model (IMM) filter is a popular method for estimating systems, whose model changes according to a finite-state, discrete-time Markov chain. In this paper, four improved IMM algorithms (IMM-EKF, IMM-CKF, IMM-UKF and IMM-CIF) are compared for nonlinear maneuvering target tracking problem. IMM disposes all the models simultaneously through Markov Chain, which can enhance the quick response of the filter. The designing of the IMM model filters is analyzed in this work, and we compare the performance of the four IMM-Improved methods for the coordinated turn model and bearing only tracking of a maneuvering target separately. We analyze the differences in the performance of the four IMM- filters using simulated data.