Bias and resolution of the vector space methods in the presence of coherent planewaves

The high resolution vector space methods of angle-of-arrival estimation which are based on the eigendecomposition of the covariance matrix of the received signal are known to break down when the incoming signals are fully correlated. In order to alleviate this problem, the covariance matrix is preprocessed using spatial smoothing averaging. An alternative method which is based on Singular Value Decomposition of data matrix is capable of resolving coherent plane waves with no need for any preprocessing. In this paper we have studied the characteristics of the MUSIC and the SVD-based Minimum Norm methods in terms of their bias and resolution in the coherent signal environment. Using Monte Carlo simulations we have shown that the Minimum Norm method has better capability in resolving coherent signals than the MUSIC algorithm, provided that the variations in beam pattern due to the inter-signal phase can be tolerated.