Fuzzy extended Kalman filter to estimate Rayleigh fading channel with PSAM for MIMO-OFDM

The Extended Kalman Filter (EKF) based on the probability theory, has been proposed to estimate the time varying channel for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) non-linear systems. However, due to inadequate information and poor estimations of the noise statistics the filter performance may be degraded. In order to reduce the errors and to increase the filter performance, a new fuzzy extended Kalman filter (FEKF), based on the priori estimation, is proposed in this work. The novelty of this paper comes from the fact that by using possibility distribution techniques, instead of Gaussian distribution techniques, a fuzzy description of the expected state and measurement is sufficient to obtain a good estimation. Compared with the EKF, the proposed algorithm can be used not only to deal with fuzzy uncertainty, but also used to handle asymmetries and to estimate the states more accurately. Simulated results shows that FEKF has better estimation precision than that of the EKF.