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A cross-layer, near-optimal soft-decision equalization approach for frequency selective multiinput multioutput (MIMO) communication systems is proposed in this paper. 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 within a subblock of the received data instead of over the whole 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, is properly modeled as the state vector of a linear system, and dynamically tracked by Kalman filtering. The performance of Kalman-PDA is further enhanced by applying a simple automatic repeat request (ARQ) protocol. The ARQ-aided MIMO detection algorithm can adjust to any preset retransmission rate to ensure a desired performance and data-rate trade-off.