A Computationally Efficient Subspace Algorithm for 2-D DOA Estimation with L-shaped Array

In this letter, a computationally efficient subspace algorithm is developed for two-dimensional (2-D) direction-of-arrival (DOA) estimation with L-shaped array structured by two uniform linear arrays (ULAs). The proposed method requires neither constructing the correlation matrix of the received data nor performing the singular value decomposition (SVD) of the correlation matrix. The problem is solved by dealing with three vectors composed of the first column, the first row and diagonal entries of the correlation matrix, which reduces the computational burden. Simultaneously, the proposed method utilizes the conjugate symmetry to enlarge the effective array aperture, which improves the estimation precision. The simulation results are presented to validate the effectiveness of the proposed algorithm.