Tensor-Based 2-D DOA Estimation for L-Shaped Nested Array
Feng Xu, Hang Zheng, Sergiy A. Vorobyov
Abstract
Among various sensor array configurations, the L -shaped nested array offers improved performance for two-dimensional (2D) direction-of-arrival (DOA) estimation through co-array processing. However, conventional methods overlook the multi-dimensional signal structure and fail to eliminate the cross term generated from the correlated co-array signal and noise components. It leads to significant degradation in DOA estimation performance. To deal with this problem, an iterative 2D DOA estimation algorithm based on tensor modeling is proposed. It is capable to eliminate the cross term. Specifically, the co-array signals of virtual subarrays on orthogonal directions are derived and concatenated to construct a higher-order tensor, whose factor matrices have the Vandermonde structure and preserve the interconnected azimuth and elevation information. A computationally efficient tensor decomposition method is then developed to independently estimate the azimuth and elevation angles, which are effectively paired using the spatial cross-correlation matrix. Furthermore, after investigating the cross term effect, a two-step iterative algorithm is proposed to sequentially estimate and remove the cross term based on the initial estimates obtained from the high-order tensor decomposition. Consequently, the 2D DOA estimation with enhanced estimation accuracy, resolution and moderate computational complexity is achieved for the L -shaped nested array. Simulation results demonstrate the superiority of the proposed algorithm over competing methods.