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The decision-directed space-alternating generalized expectation-maximization (SAGE) algorithm is introduced in  to estimate the channel and track the channel varying for OFDM systems with transmitter diversity. However, this method is based upon a discrete Fourier transform (DFT), which will cause power leakage and result in an error floor in a multipath channel with non-sample-spaced time delays. In order to overcome this problem, a low rank approximation method is presented by using the signal subspace of the channel frequency autocorrelation matrix. Furthermore, a modified fast subspace tracking algorithm is introduced to adaptive estimate the signal subspace by using a space–time block coded training blocks sent at regular interval. Simulation results show the advantages of the proposed technique for MIMO-OFDM systems in time-varying non-sample-spaced wireless channels.