Resolving manifold ambiguities in direction-of-arrival estimation for nonuniform linear antenna arrays

This paper addresses the problem of ambiguities in direction-of-arrival (DOG) estimation for nonuniform (sparse) linear arrays. Usually, DOA estimation ambiguities are associated with linear dependence among the points on the antenna array manifold, that is, the steering vectors degenerate so that each mag be expressed as a linear combination of the others. Most nonuniform array geometries, including the so-called "minimum-redundancy" arrays, admit such manifold ambiguities; While the standard subspace algorithms such as MUSIC fail to provide unambiguous DOA estimates under these conditions, we demonstrate that this failure does not necessarily imply that consistent and asymptotically effective DOA estimates do not exist. We demonstrate that in most cases involving uncorrelated Gaussian sources, manifold ambiguity does not necessarily imply nonidentifiability; most importantly, we introduce algorithms designed to resolve manifold ambiguity. We also show that for situations where the number of sources exceeds the number of array sensors, a new class of locally nonidentifiable scenario exists.