2D-DOA Estimation Based on Higher-Order SVD-Based Using EMVS Sparse Array

In this paper, we propose a tensor-based subspace algorithm for the two-dimensional direction of arrival (2D-DOA) estimation using a sparse array equipped with electromagnetic vector sensors (EMVS). Our approach capitalizes on the multidimensional characteristics of the collected data by arranging its covariance into a fourth-order tensor. Through the application of higher-order singular value decomposition, we improve signal subspace estimation compared to existing methods. To further enhance our algorithm, we integrate spatial rotation invariance techniques and vector cross-product methods. This combination enables automatic angle estimation without the need for pairing and without compromising aperture loss. Our proposed algorithm exhibits superior estimation performance, particularly in challenging scenarios characterized by low signal-to-noise ratios and limited snapshot availability. To validate the effectiveness and enhancements of our approach, we conduct numerical simulation experiments.