T-S Fuzzy Multiple Model Target Tracking Algorithm with UKF Parameter Identification

A novel T-S Fuzzy Multiple Model Target Tracking Algorithm with UKF Parameter Identification(TS-UKF) is proposed to solve the uncertainty problem of maneuvering target dynamic model in nonlinear systems. Firstly, the target feature information is represented by multiple semantic fuzzy sets, and a general T-S fuzzy semantic multiple model framework is constructed. Then, the fuzzy C regression clustering algorithm is used to identify the premise parameters of the T-S fuzzy semantic multiple model. Meanwhile, to realize the nonlinear characteristics of the system, the unscented Kalman filtering algorithm is introduced to identify the consequence parameters. Simulation results show that the proposed algorithm has better tracking performance than the traditional interacting multiple model algorithm and interacting multiple model unscented Kalman filter. When the direction of the target is changed suddenly or the dynamic prior information of the target is not accurate, it can effectively track the target.