  In this paper, we propose a novel reduced-rank algorithm for direction of
arrival (DOA) estimation based on the minimum variance (MV) power spectral
evaluation. It is suitable to DOA estimation with large arrays and can be
applied to arbitrary array geometries. The proposed DOA estimation algorithm is
formulated as a joint optimization of a subspace projection matrix and an
auxiliary reduced-rank parameter vector with respect to the MV and grid search.
A constrained least squares method is employed to solve this joint optimization
problem for the output power over the grid. The proposed algorithm is described
for problems of large number of users' direction finding with or without exact
information of the number of sources, and does not require the singular value
decomposition (SVD). The spatial smoothing (SS) technique is also employed in
the proposed algorithm for dealing with correlated sources problem. Simulations
are conducted with comparisons against existent algorithms to show the improved
performance of the proposed algorithm in different scenarios.
