  In this article, the outlier production mechanism of the conventional
Multiple Signal Classification (MUSIC) and the g-MUSIC Direction-of-Arrival
(DoA) estimation technique is investigated using tools from Random Matrix
Theory (RMT). A general Central Limit Theorem (CLT) is derived that allows to
analyze the asymptotic stochastic behavior of eigenvector-based cost functions
in the asymptotic regime where the number of snapshots and the number of
antennas increase without bound at the same rate. Furthermore, this CLT is used
to provide an accurate prediction of the resolution capabilities of the MUSIC
and the g-MUSIC DoA estimation method. The finite dimensional distribution of
the MUSIC and the g-MUSIC cost function is shown to be asymptotically jointly
Gaussian distributed in the asymptotic regime.
