A De-noising 2-D DOA Estimation Method for Random EMVS Arrays

This paper addresses the two-dimensional direction-of-arrival(2D-DOA) estimation problem using an Electromagnetic Vector Sensor (EMVS) array, where an arbitrary array geometry and the presence of unknown nonuniform noise are considered. A novel algorithm combining tensor completion and Parallel Factor Decomposition (PARAFAC) is proposed to improve the accuracy of estimation. Firstly, a covariance tensor is constructed. The impact of non-uniform noise is eliminated via removing the noise-affected elements of the covariance tensor. The noise-free covariance matrix is then recovered through tensor completion, and the target angles are extracted using PARAFAC algorithm. Additionally, Cramer-Rao Bounds (CRB) for joint 2D-DOA and polarization parameter estimation are derived, providing a theoretical basis for array design. Simulation results demonstrate that the proposed algorithm is robust to nonuniform noise and achieves high-resolution 2D-DOA estimation, outperforming traditional methods in terms of accuracy. Comparative evaluations confirm the superiority of the proposed geometry and denoising approach.