In this paper, we propose a new pilot-aided channel estimator. Among the existing approaches, some are based on adaptive algorithms, but they are outperformed by methods where the channel is modeled by an AR or an ARMA process. In that case, estimating the model parameters from noisy observations and selecting the model orders are challenging problems. To avoid them, we propose to view the channel estimation as a realization issue. By taking advantage of the subspace methods for identification, the proposed estimator provides the system matrices in the state-space representation of the channel directly from the output observations. At that stage, the channel process can be estimated using a Kalman filter. This method has the advantage of being non-iterative and avoiding an a priori model for the channel