Selecting the best among several estimates in DOA estimation

In this work we present a new direction of arrival (DOA) estimation scheme that applies to uniform linear arrays. We propose to get several estimates by applying the Pisarenko harmonic retrieval (PHR) method to different sets of covariances and to select the best estimate by using the maximum likelihood (ML) criterion. More generally we would like to promote the idea that it is often more efficient to select the best among several estimates rather than to combine them. This requires of course the existence of a decision criterion which indeed conveys additional information.