Weighted Subspace Fitting for General Array Error Models

Model error sensitivity is an issue common to all high resolution direction of arrival estimators. Much attention has been directed to the design of algorithms for minimum variance estimation taking only finite sample errors into account. Approaches to reduce the sensitivity due to array calibration errors have also appeared in the literature. Herein, one such approach is adopted which assumes that the errors due to finite samples and model errors are of comparable size. Minimum variance estimators have previously been proposed for this case. These estimators typically lead to non-linear optimization problems and are not in general consistent if the source signals are fully correlated. For special error models, subspace fitting methods have previously been devised. In this paper, a weighted subspace fitting method for general array perturbation models is derived. This method provides minimum variance estimates under the assumption that the prior distribution of the perturbation model i..