Blind Source Separation over Space

We propose a new estimation method for the spatial blind source separation model. The new estimation is based on an eigenanalysis of a positive definite matrix defined in terms of multiple spatial local covariance matrices, and, therefore, can handle moderately high-dimensional random fields. The consistency of the estimated mixing matrix is established with explicit error rates even when the eigen-gap decays to zero slowly. The proposed method is illustrated via both simulation and a real data example.