Joint estimation strategy with application to eigenstructure methods

Numerous authors have attempted to improve the performance of eigenstructure methods, but all these approaches do not employ the additive information arising when several direction of arrival (DOA) estimation algorithms (referred to as underlying estimators) are used simultaneously. We show that involving this information, one can achieve much better DOA estimation performance than that of each underlying estimator used separately. We introduce a joint estimation strategy (JES) which represents a simple and effective way of extracting and combining such information. This strategy is then applied to the set of eigenstructure underlying DOA estimators including the MUSIC and generalized min-norm (GMN) estimators