A 2-D DOA Estimation Method With Reduced Complexity in Unfolded Coprime L-Shaped Array

In this article, we focus on investigating the two-dimensional direction of arrival (2-D DOA) estimation for the unfolded coprime L-shaped array (UCLsA). Due to 2-D spectral peak searching, the computational complexity of the existing 2-D DOA estimation methods based on the multiple signal classification (MUSIC) algorithm is too high. Motivated by this, we propose a novel low-complexity 2-D DOA estimation method based on MUSIC, where the 2-D spectral peak searching is converted to 1-D searching by constructing a transform domain. Moreover, the proposed method makes full use of the received signals to calculate the noise subspace, which can take advantages of the large array aperture and mutual information of the UCLsA and, significantly, improve the estimation accuracy. Simulation results demonstrate that: 1) the proposed method achieves similar performance to the Cramér-Rao bound (CRB); 2) the proposed method outperforms the existing 2-D DOA estimation algorithms; and 3) the implementation cost of the proposed method is lower than those of the existing methods.