Subspace-based direction-of-arrival estimation for more sources than sensors using planar arrays

We propose a novel subspace-based direction-of-arrival (DOA) estimation method and an associated planar array geometry optimization technique. The proposed DOA estimation approach allows to estimate the DOAs of more sources than sensors and to resolve manifold ambiguities in the case of uncorrelated signals. It is related to the covariance augmentation (CA) technique, but in contrast to the CA technique, it can be applied to non-uniform planar array geometries.