Maneuvering Target Tracking using T-S Fuzzy Model of Physical Membership Function

A T-S fuzzy model identification method based on physical membership function is proposed for maneuvering target tracking. T-S fuzzy model is a good tool for fitting complex and nonlinear systems conducted by separating inputs–outputs spaces of systems and identifying corresponding parameters. Usually membership degrees play important roles in fusing local fuzzy models for reflecting nonlinear property of T-S fuzzy model, however, usual membership degrees are obtained by Gaussian function, which not only lacks interpretability and meanings but also is complex to be used. In this paper, a physical membership function with interpretability and physical meanings is proposed. To identify T-S fuzzy model based on the proposed method, first, a hyper-planed FSC algorithm as the separating method is utilized. Then UKF is used to identify consequent parameters. Finally, the proposed physical membership function is used to fuse local models and estimate final states. We apply the proposed T-S fuzzy model algorithms to maneuvering target tracking, and comparisons with several classical methods on both simulated data and real data demonstrate effectiveness and advantages of the proposed methods in tracking accuracy.