Direction-of-Arrival Estimation using a Low-Complexity Covariance-Based Approach

This article presents a new algorithm for performing direction-of-arrival (DOA) estimation using manipulations on covariance matrices. The proposed algorithm combines a new formulation for data projection on real subspaces, together with beamspace decompositions, reducing the sizes of all data structures and computational complexity of the resulting estimation process. Theoretical analyses as well as computer simulations indicate that the proposed algorithm reduces its ESPRIT equivalent computational complexity by a minimum of 20%, while presenting similar mean-square error (MSE) performance.