Joint carrier frequency offset (CFO) and channel estimation for uplink MIMO-OFDMA systems over time-varying channels is investigated. To cope with the prohibitive computational complexity involved in estimating multiple CFOs and channels, pilot-assisted and semi-blind schemes comprised of parallel Schmidt Extended Kalman filters (SEKFs) and Schmidt-Kalman Approximate Particle Filters (SK-APF) are proposed. In the SK-APF, a Rao-Blackwellized particle filter (RBPF) is developed to first estimate the nonlinear state variable, i.e. the desired user's CFO, through the sampling-importance-resampling (SIRS) technique. The individual user channel responses are then updated via a bank of Kalman filters conditioned on the CFO sample trajectories. Simulation results indicate that the proposed schemes can achieve highly accurate CFO/channel estimates, and that the particle filtering approach in the SK-APF outperforms the more conventional Schmidt Extended Kalman Filter.