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

TL;DR

This paper introduces a reduced-rank DOA estimation algorithm that combines joint subspace optimization and grid search, improving performance for large arrays and correlated sources without requiring SVD.

Contribution

It presents a novel reduced-rank DOA estimation method based on joint optimization and grid search, applicable to arbitrary array geometries and correlated sources.

Findings

Improved DOA estimation accuracy in large array scenarios.

Effective handling of correlated sources with spatial smoothing.

No need for singular value decomposition (SVD).

Abstract

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.