Subspace estimation using factor analysis

Many subspace estimation techniques assume either that the system has a calibrated array or that the noise covariance matrix is known. If the noise covariance matrix is unknown, training or other calibration techniques are used to find it. In this paper another approach to the problem of unknown noise covariance is presented. The complex factor analysis (FA) and a new extended version of this model are used to model the covariance matrix. The steep algorithm for finding the MLE of the model parameters is presented. The Fisher information and an expression for the Cramér-Rao bound are derived. The practical use of the model is illustrated using simulated and experimental data.