Tensor decomposition estimator applied to coherent targets in EMVS-MIMO radar

Abstract

Electromagnetic vector sensor (EMVS) arrays in multiple-input multiple-output (MIMO) radar systems have gained substantial attention for their enhanced capabilities in target detection and localization over the past decade. However, conventional direction of arrival (DOA) estimation algorithms exhibit significant limitations when processing coherent targets. In response to these challenges, this paper introduces a novel algorithm leveraging tensor decomposition to enhance DOA estimation accuracy within monostatic EMVS-MIMO radar systems. Initially, we employ a specific sequence to rearrange the original array output, thereby linking the source matrix with the spatial response of the transmitting or receiving array. This rearrangement effectively addresses the rank deficiency issue in the covariance matrix. Subsequently, the restructured model undergoes parallel factor (PARAFAC) decomposition to yield high-precision estimates of the factor matrices. Ultimately, 2D-DOA estimation is accomplished by applying the normalized vector cross product (NVCP) technique to the polarization response factor matrices. In contrast to existing algorithms for coherent targets estimation, the proposed method ensures no information loss, achieves superior angle estimation accuracy, and is applicable to arbitrary sensor geometries. Numerical simulations substantiate the effectiveness and superiority of the proposed algorithm.