An improved music algorithm based on low rank perturbation of large random matrices

This paper is devoted to subspace DoA estimation, when the number of available snapshots N is of the same order of magnitude as the number of sensors M. In this context, traditional subspace methods fail because the empirical covariance matrix of the observations is a poor estimate of the true covariance matrix. The goal of the paper is to propose a new consistent estimator of the DoAs in the case where M, N → + ∞ at the same rate, using large random matrix theory. It is assumed that the number of sources is constant, and recent results on the so called spiked matrix models are used. First and second order results are provided.