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We propose A low-complexity, near-optimal soft-decision equalization approach for frequency selective multiinput multioutput (MIMO) communication systems. With the help of iterative processing, two detection and estimation schemes based on second-order statistics are harmoniously put together to yield a two-part receiver structure: local multiuser detection (MUD) using soft-decision probabilistic data association (PDA) detection, and dynamic noise-interference tracking using Kalman filtering. The proposed Kalman-PDA approach performs local MUD to reduce the computational load, while all the interference affecting the local subblock is dynamically tracked by Kalman filtering, to maintain near-optimum detection performance. Two types of Kalman filters are designed, both of which are able to track a finite impulse response (FIR) MIMO channel of any memory length. The overall algorithms enjoy low complexity that is only polynomial in the number of information-bearing bits to be detected, regardless of the data block size. The near-optimum detection performance is testified via simulations.