Robust subspace estimation using prior information

We derive robust estimators of the parameters in a linear subspace model. Like total least squares (TLS), these estimators allow for errors in both the data and in the subspace model. However, unlike total least squares, these estimators allow the perturbation of the model to be constrained. These constraints have simple geometric interpretations and allow for various levels of confidence in the a priori signal model. The estimators of this paper are also distinguished from the TLS in that they are invariant to certain arbitrary scalings and rotations of the signal model. This property, which the TLS does not possess, is shown to be essential for certain estimation problems.