WEIGHTED SUBSPACE FITTING FOR GENERAL ARRAY ERROR MODELS

Model error sensitivity is an issue common to all high-resolution dire ction-of-arrival estimators. Much attention has been directed to the d esign of algorithms for minimum variance estimation taking only finite sample errors into account. Approaches to reduce the sensitivity due to army calibration errors have also appeared in the literature. Herei n, one such approach is adopted that assumes that the errors due to fi nite samples and model errors are of comparable size. A weighted subsp ace fitting method for very general array perturbation models is deriv ed. This method provides minimum variance estimates under the assumpti on that the prior distribution of the perturbation model is known. Int erestingly, the method reduces to the WSF (MODE) estimator if no model errors are present, Vice versa, assuming that model errors dominate, the method specializes to the corresponding ''model-errors-only subspa ce fitting method.'' Unlike previous techniques for model errors, the estimator can be implemented using a two-step procedure if the nominal array is uniform and linear, and it is also consistent even if the si gnals are fully correlated. The paper also contains a large sample ana lysis of one of the alternative methods, namely, MAPprox, It is shown that MAPprox also provides minimum variance estimates under reasonable assumptions.