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This paper develops a low-complexity soft-decision equalization approach for frequency selective multi-input multi-output (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 detector performs local MUD within a subblock of the received data instead of over the entire data set to reduce the computational load. At the same time, all the interference affecting the local subblock, including both multiple access and intersymbol interference (ISI), is properly modeled as the state vector of a linear system and dynamically tracked by Kalman filtering. 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. Furthermore, we introduce two optional performance-enhancing techniques: Kalman-PDA with cross-layer automatic repeat request (ARQ) for uncoded systems and code-aided Kalman-PDA for coded systems. Simulations show that both techniques can render error performance that is better than Kalman-PDA alone and competitive to sphere decoding.