A DOA Estimation Method for UCA Based on Orthogonal Subspace Compensation

In this letter, the problem of 2-D direction of arrival (DOA) estimation using uniform circular array (UCA) is addressed. The performance of the beamspace transform (BT) based algorithm is degraded when shrinking the quantity of antennas because the BT raises the residual error. To overcome this problem, we propose a 2-D compensation method. Firstly, we estimate the biased 2-D DOAs via UCA-ESPRIT algorithm. Secondly, the eigenvalue decomposition (EVD) of covariance matrix without the BT is conducted to extract the noise subspace in element space. Thereafter, inspired by the relationship that the steering vector is orthogonal to the noise subspace, the first-order Taylor expansion is performed for the direction matrix which is reconstructed by the biased DOA estimates. Finally, the offsets are compensated via least squares (LS). Compared to virtual array transformation methods, the proposed method possesses better estimation performance and needs less computation cost. Multiple simulation experiments are presented to verify the efficiency of the approach.

Automated summary

The letter addresses the issue of 2-D direction of arrival (DOA) estimation using uniform circular array (UCA). It proposes a 2-D compensation method that utilizes UCA-ESPRIT algorithm for biased DOA estimation and eigenvalue decomposition for noise subspace extraction. The method outperforms virtual array transformation methods in estimation accuracy and computational efficiency, as confirmed by multiple simulation experiments.