A Real-time Interactive Multi-Model (RT-IMM) Target Tracking Method

Abstract

The IMM target tracking theory employs the invariant Markov transfer probability matrix and the residual model in the model probability update, which lacks real-time adaptability. In this paper, Bayesian estimation theory is utilized to update the target state distribution by integration with multi-model tracking results, and the model probability of next moment is updated according to the model likelihood function. The likelihood function theory is applied to model probability interactive update, and the current filtering model target state distribution is employed to update the Markov transfer probability matrix among models. The proposed method is compared with conventional IMM method through Monte Carlo simulation. The simulation results show that the accuracy of the proposed method is better than conventional IMM, and it can track the maneuvering targets and fluctuating targets effectively.