Application of Sigma-point Kalman filters on carrier frequency offset estimation of OFDM systems

For the non-linear dynamic state-space model, extended Kalman filter (EKF) fits the system state and observation equations to obtain valuation of state but it has deficiencies like apparent fluctuation and slow convergence. While the Sigma-point Kalman filters obtain the statistical characteristics based on deterministic samples, accordingly better approximation can be achieved. In this paper, the OFDM carrier frequency offset is described as non-linear dynamic state-space model, and the Sigma-point Kalman filters are applied to estimate the offset value. Simulation results show that they perform better at capturing the high order moments than EKF and they gained higher accuracy, faster convergence, smaller fluctuations and less noise sensitivity.