This letter deals with the identification of timevarying Rayleigh fading channels using a training sequence based approach. When the fading channel is approximated by an autoregressive (AR) process, it can be estimated by means of Kalman filtering for instance. However, this method requires the estimations of both the AR parameters and the noise variances in the state space representation of the system. For this purpose, the existing noise compensated approaches could be considered, but they usually require a long observation window and do not necessarily provide reliable estimates when the signal-tonoise ratio is low. Therefore, we propose to view the channel identification as an errors-in-variables (EIV) issue. The method consists in searching the noise variances that enable specific noise compensated autocorrelation matrices of observations to be positive semidefinite. In addition, the AR parameters can be estimated from the null spaces of these matrices. Simulation results confirm the effectiveness of this approach especially in presence of a high amount of noise.