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In this paper, we propose a novel reduced-rank algorithm for direction ofarrival (DOA) estimation based on the minimum variance (MV) power spectralevaluation. It is suitable to DOA estimation with large arrays and can beapplied to arbitrary array geometries. The proposed DOA estimation algorithm isformulated as a joint optimization of a subspace projection matrix and anauxiliary reduced-rank parameter vector with respect to the MV and grid search.A constrained least squares method is employed to solve this joint optimizationproblem for the output power over the grid. The proposed algorithm is describedfor problems of large number of users' direction finding with or without exactinformation of the number of sources, and does not require the singular valuedecomposition (SVD). The spatial smoothing (SS) technique is also employed inthe proposed algorithm for dealing with correlated sources problem. Simulationsare conducted with comparisons against existent algorithms to show the improvedperformance of the proposed algorithm in different scenarios.

Reduced-Rank DOA Estimation based on Joint Iterative Subspace Optimization and Grid Search