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Channel estimation is an important issue for wireless communication systems. A channel estimation scheme using a Takagi-Sugeno (T-S) fuzzy-based Kalman filter under the time-varying velocity of the mobile station in a multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system is proposed in this paper. The fuzzy technique is used to interpolate several linear models to approximate the nonlinear estimation system. A MIMO system with the orthogonal space-time block coding (OSTBC) scheme is considered, where the radio channel is modeled as an autoregressive (AR) random process. The parameters of the AR process and the channel gain are simultaneously estimated by the proposed method. One-step-ahead prediction can be obtained during this estimation procedure. This is useful for the decision-directed channel-tracking design, particularly in the fast-fading channel. Furthermore, the robust minimum mean-square error (MMSE) equalization design can be achieved by considering the channel prediction error to improve the performance of symbol detection. To validate the performance of our proposed method, several simulation results are given and compared with those of other methods. When considering the time-varying velocity of the mobile station communication in the MIMO-OFDM system, the enhanced equalizer based on the T-S fuzzy-based Kalman filter performs better than those based on the conventional channel estimators in terms of symbol error rate.